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SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer | Xuanyao Chen, Zhijian Liu, Haotian Tang, Li Yi, Hang Zhao, Song Han | High-resolution images enable neural networks to learn richer visual representations. However, this improved performance comes at the cost of growing computational complexity, hindering their usage in latency-sensitive applications. As not all pixels are equal, skipping computations for less-important regions offers a simple and effective measure to reduce the computation. This, however, is hard to be translated into actual speedup for CNNs since it breaks the regularity of the dense convolution workload. In this paper, we introduce SparseViT that revisits activation sparsity for recent window-based vision transformers (ViTs). As window attentions are naturally batched over blocks, actual speedup with window activation pruning becomes possible: i.e., 50% latency reduction with 60% sparsity. Different layers should be assigned with different pruning ratios due to their diverse sensitivities and computational costs. We introduce sparsity-aware adaptation and apply the evolutionary search to efficiently find the optimal layerwise sparsity configuration within the vast search space. SparseViT achieves speedups of 1.5x, 1.4x, and 1.3x compared to its dense counterpart in monocular 3D object detection, 2D instance segmentation, and 2D semantic segmentation, respectively, with negligible to no loss of accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_SparseViT_Revisiting_Activation_Sparsity_for_Efficient_High-Resolution_Vision_Transformer_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.17605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_SparseViT_Revisiting_Activation_Sparsity_for_Efficient_High-Resolution_Vision_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_SparseViT_Revisiting_Activation_Sparsity_for_Efficient_High-Resolution_Vision_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Prototype-Based Embedding Network for Scene Graph Generation | Chaofan Zheng, Xinyu Lyu, Lianli Gao, Bo Dai, Jingkuan Song | Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category, e.g., "man-eating-pizza, giraffe-eating-leaf", and the severe inter-class similarity between different classes, e.g., "man-holding-plate, man-eating-pizza", in model's latent space. The above challenges prevent current SGG methods from acquiring robust features for reliable relation prediction. In this paper, we claim that predicate's categoryinherent semantics can serve as class-wise prototypes in the semantic space for relieving the above challenges caused by the diverse visual appearances. To the end, we propose the Prototype-based Embedding Network (PE-Net), which models entities/predicates with prototype-aligned compact and distinctive representations and establishes matching between entity pairs and predicates in a common embedding space for relation recognition. Moreover, Prototypeguided Learning (PL) is introduced to help PE-Net efficiently learn such entity-predicate matching, and Prototype Regularization (PR) is devised to relieve the ambiguous entity-predicate matching caused by the predicate's semantic overlap. Extensive experiments demonstrate that our method gains superior relation recognition capability on SGG, achieving new state-of-the-art performances on both Visual Genome and Open Images datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_Prototype-Based_Embedding_Network_for_Scene_Graph_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_Prototype-Based_Embedding_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Prototype-Based_Embedding_Network_for_Scene_Graph_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Prototype-Based_Embedding_Network_for_Scene_Graph_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Toward RAW Object Detection: A New Benchmark and a New Model | Ruikang Xu, Chang Chen, Jingyang Peng, Cheng Li, Yibin Huang, Fenglong Song, Youliang Yan, Zhiwei Xiong | In many computer vision applications (e.g., robotics and autonomous driving), high dynamic range (HDR) data is necessary for object detection algorithms to handle a variety of lighting conditions, such as strong glare. In this paper, we aim to achieve object detection on RAW sensor data, which naturally saves the HDR information from image sensors without extra equipment costs. We build a novel RAW sensor dataset, named ROD, for Deep Neural Networks (DNNs)-based object detection algorithms to be applied to HDR data. The ROD dataset contains a large amount of annotated instances of day and night driving scenes in 24-bit dynamic range. Based on the dataset, we first investigate the impact of dynamic range for DNNs-based detectors and demonstrate the importance of dynamic range adjustment for detection on RAW sensor data. Then, we propose a simple and effective adjustment method for object detection on HDR RAW sensor data, which is image adaptive and jointly optimized with the downstream detector in an end-to-end scheme. Extensive experiments demonstrate that the performance of detection on RAW sensor data is significantly superior to standard dynamic range (SDR) data in different situations. Moreover, we analyze the influence of texture information and pixel distribution of input data on the performance of the DNNs-based detector. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Toward_RAW_Object_Detection_A_New_Benchmark_and_a_New_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Toward_RAW_Object_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Toward_RAW_Object_Detection_A_New_Benchmark_and_a_New_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Toward_RAW_Object_Detection_A_New_Benchmark_and_a_New_CVPR_2023_paper.html | CVPR 2023 | null |
Music-Driven Group Choreography | Nhat Le, Thang Pham, Tuong Do, Erman Tjiputra, Quang D. Tran, Anh Nguyen | Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present AIOZ-GDANCE, a new largescale dataset for music-driven group dance generation. Unlike existing datasets that only support single dance, our new dataset contains group dance videos, hence supporting the study of group choreography. We propose a semiautonomous labeling method with humans in the loop to obtain the 3D ground truth for our dataset. The proposed dataset consists of 16.7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres. We show that naively applying single dance generation technique to creating group dance motion may lead to unsatisfactory results, such as inconsistent movements and collisions between dancers. Based on our new dataset, we propose a new method that takes an input music sequence and a set of 3D positions of dancers to efficiently produce multiple group-coherent choreographies. We propose new evaluation metrics for measuring group dance quality and perform intensive experiments to demonstrate the effectiveness of our method. Our project facilitates future research on group dance generation and is available at https://aioz-ai.github.io/AIOZ-GDANCE/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Le_Music-Driven_Group_Choreography_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Le_Music-Driven_Group_Choreography_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.12337 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Le_Music-Driven_Group_Choreography_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Le_Music-Driven_Group_Choreography_CVPR_2023_paper.html | CVPR 2023 | null |
Cascade Evidential Learning for Open-World Weakly-Supervised Temporal Action Localization | Mengyuan Chen, Junyu Gao, Changsheng Xu | Targeting at recognizing and localizing action instances with only video-level labels during training, Weakly-supervised Temporal Action Localization (WTAL) has achieved significant progress in recent years. However, living in the dynamically changing open world where unknown actions constantly spring up, the closed-set assumption of existing WTAL methods is invalid. Compared with traditional open-set recognition tasks, Open-world WTAL (OWTAL) is challenging since not only are the annotations of unknown samples unavailable, but also the fine-grained annotations of known action instances can only be inferred ambiguously from the video category labels. To address this problem, we propose a Cascade Evidential Learning framework at an evidence level, which targets at OWTAL for the first time. Our method jointly leverages multi-scale temporal contexts and knowledge-guided prototype information to progressively collect cascade and enhanced evidence for known action, unknown action, and background separation. Extensive experiments conducted on THUMOS-14 and ActivityNet-v1.3 verify the effectiveness of our method. Besides the classification metrics adopted by previous open-set recognition methods, we also evaluate our method on localization metrics which are more reasonable for OWTAL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Cascade_Evidential_Learning_for_Open-World_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Cascade_Evidential_Learning_for_Open-World_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Cascade_Evidential_Learning_for_Open-World_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Movie Scene Detection Using State-Space Transformers | Md Mohaiminul Islam, Mahmudul Hasan, Kishan Shamsundar Athrey, Tony Braskich, Gedas Bertasius | The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition models, which are typically designed for short-range video analysis. This work proposes a State-Space Transformer model that can efficiently capture dependencies in long movie videos for accurate movie scene detection. Our model, dubbed TranS4mer, is built using a novel S4A building block, which combines the strengths of structured state-space sequence (S4) and self-attention (A) layers. Given a sequence of frames divided into movie shots (uninterrupted periods where the camera position does not change), the S4A block first applies self-attention to capture short-range intra-shot dependencies. Afterward, the state-space operation in the S4A block is used to aggregate long-range inter-shot cues. The final TranS4mer model, which can be trained end-to-end, is obtained by stacking the S4A blocks one after the other multiple times. Our proposed TranS4mer outperforms all prior methods in three movie scene detection datasets, including MovieNet, BBC, and OVSD, while also being 2x faster and requiring 3x less GPU memory than standard Transformer models. We will release our code and models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Islam_Efficient_Movie_Scene_Detection_Using_State-Space_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Islam_Efficient_Movie_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Islam_Efficient_Movie_Scene_Detection_Using_State-Space_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Islam_Efficient_Movie_Scene_Detection_Using_State-Space_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline | Wei Ji, Jingjing Li, Cheng Bian, Zongwei Zhou, Jiaying Zhao, Alan L. Yuille, Li Cheng | Robust and reliable semantic segmentation in complex scenes is crucial for many real-life applications such as autonomous safe driving and nighttime rescue. In most approaches, it is typical to make use of RGB images as input. They however work well only in preferred weather conditions; when facing adverse conditions such as rainy, overexposure, or low-light, they often fail to deliver satisfactory results. This has led to the recent investigation into multispectral semantic segmentation, where RGB and thermal infrared (RGBT) images are both utilized as input. This gives rise to significantly more robust segmentation of image objects in complex scenes and under adverse conditions. Nevertheless, the present focus in single RGBT image input restricts existing methods from well addressing dynamic real-world scenes. Motivated by the above observations, in this paper, we set out to address a relatively new task of semantic segmentation of multispectral video input, which we refer to as Multispectral Video Semantic Segmentation, or MVSS in short. An in-house MVSeg dataset is thus curated, consisting of 738 calibrated RGB and thermal videos, accompanied by 3,545 fine-grained pixel-level semantic annotations of 26 categories. Our dataset contains a wide range of challenging urban scenes in both daytime and nighttime. Moreover, we propose an effective MVSS baseline, dubbed MVNet, which is to our knowledge the first model to jointly learn semantic representations from multispectral and temporal contexts. Comprehensive experiments are conducted using various semantic segmentation models on the MVSeg dataset. Empirically, the engagement of multispectral video input is shown to lead to significant improvement in semantic segmentation; the effectiveness of our MVNet baseline has also been verified. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_Multispectral_Video_Semantic_Segmentation_A_Benchmark_Dataset_and_Baseline_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ji_Multispectral_Video_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Multispectral_Video_Semantic_Segmentation_A_Benchmark_Dataset_and_Baseline_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Multispectral_Video_Semantic_Segmentation_A_Benchmark_Dataset_and_Baseline_CVPR_2023_paper.html | CVPR 2023 | null |
Reducing the Label Bias for Timestamp Supervised Temporal Action Segmentation | Kaiyuan Liu, Yunheng Li, Shenglan Liu, Chenwei Tan, Zihang Shao | Timestamp supervised temporal action segmentation (TSTAS) is more cost-effective than fully supervised counterparts. However, previous approaches suffer from severe label bias due to over-reliance on sparse timestamp annotations, resulting in unsatisfactory performance. In this paper, we propose the Debiasing-TSTAS (D-TSTAS) framework by exploiting unannotated frames to alleviate this bias from two phases: 1) Initialization. To reduce the dependencies on annotated frames, we propose masked timestamp predictions (MTP) to ensure that initialized model captures more contextual information. 2) Refinement. To overcome the limitation of the expressiveness from sparsely annotated timestamps, we propose a center-oriented timestamp expansion (CTE) approach to progressively expand pseudo-timestamp groups which contain semantic-rich motion representation of action segments. Then, these pseudo-timestamp groups and the model output are used to iteratively generate pseudo-labels for refining the model in a fully supervised setup. We further introduce segmental confidence loss to enable the model to have high confidence predictions within the pseudo-timestamp groups and more accurate action boundaries. Our D-TSTAS outperforms the state-of-the-art TSTAS method as well as achieves competitive results compared with fully supervised approaches on three benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Reducing_the_Label_Bias_for_Timestamp_Supervised_Temporal_Action_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Reducing_the_Label_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Reducing_the_Label_Bias_for_Timestamp_Supervised_Temporal_Action_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Reducing_the_Label_Bias_for_Timestamp_Supervised_Temporal_Action_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos | Yubin Hu, Yuze He, Yanghao Li, Jisheng Li, Yuxing Han, Jiangtao Wen, Yong-Jin Liu | Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive network strategies have been proposed for efficient VSS. However, they did not consider a crucial factor that affects the computational cost from the input side: the input resolution. In this paper, we propose an altering resolution framework called AR-Seg for compressed videos to achieve efficient VSS. AR-Seg aims to reduce the computational cost by using low resolution for non-keyframes. To prevent the performance degradation caused by downsampling, we design a Cross Resolution Feature Fusion (CReFF) module, and supervise it with a novel Feature Similarity Training (FST) strategy. Specifically, CReFF first makes use of motion vectors stored in a compressed video to warp features from high-resolution keyframes to low-resolution non-keyframes for better spatial alignment, and then selectively aggregates the warped features with local attention mechanism. Furthermore, the proposed FST supervises the aggregated features with high-resolution features through an explicit similarity loss and an implicit constraint from the shared decoding layer. Extensive experiments on CamVid and Cityscapes show that AR-Seg achieves state-of-the-art performance and is compatible with different segmentation backbones. On CamVid, AR-Seg saves 67% computational cost (measured in GFLOPs) with the PSPNet18 backbone while maintaining high segmentation accuracy. Code: https://github.com/THU-LYJ-Lab/AR-Seg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Efficient_Semantic_Segmentation_by_Altering_Resolutions_for_Compressed_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Efficient_Semantic_Segmentation_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.07224 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Efficient_Semantic_Segmentation_by_Altering_Resolutions_for_Compressed_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Efficient_Semantic_Segmentation_by_Altering_Resolutions_for_Compressed_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection | Zhenglin Zhou, Huaxia Li, Hong Liu, Nanyang Wang, Gang Yu, Rongrong Ji | Recently, deep learning-based facial landmark detection has achieved significant improvement. However, the semantic ambiguity problem degrades detection performance. Specifically, the semantic ambiguity causes inconsistent annotation and negatively affects the model's convergence, leading to worse accuracy and instability prediction. To solve this problem, we propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of semantic ambiguity. We find that semantic ambiguity results in the anisotropic predicted distribution, which inspires us to use predicted distribution to represent semantic ambiguity. Based on this, we design the STAR loss that measures the anisotropism of the predicted distribution. Compared with the standard regression loss, STAR loss is encouraged to be small when the predicted distribution is anisotropic and thus adaptively mitigates the impact of semantic ambiguity. Moreover, we propose two kinds of eigenvalue restriction methods that could avoid both distribution's abnormal change and the model's premature convergence. Finally, the comprehensive experiments demonstrate that STAR loss outperforms the state-of-the-art methods on three benchmarks, i.e., COFW, 300W, and WFLW, with negligible computation overhead. Code is at https://github.com/ZhenglinZhou/STAR | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_STAR_Loss_Reducing_Semantic_Ambiguity_in_Facial_Landmark_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_STAR_Loss_Reducing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_STAR_Loss_Reducing_Semantic_Ambiguity_in_Facial_Landmark_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_STAR_Loss_Reducing_Semantic_Ambiguity_in_Facial_Landmark_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
A Meta-Learning Approach to Predicting Performance and Data Requirements | Achin Jain, Gurumurthy Swaminathan, Paolo Favaro, Hao Yang, Avinash Ravichandran, Hrayr Harutyunyan, Alessandro Achille, Onkar Dabeer, Bernt Schiele, Ashwin Swaminathan, Stefano Soatto | We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset (e.g., 5 samples per class) for extrapolation. This is because the log-performance error against the log-dataset size follows a nonlinear progression in the few-shot regime followed by a linear progression in the high-shot regime. We introduce a novel piecewise power law (PPL) that handles the two data regimes differently. To estimate the parameters of the PPL, we introduce a random forest regressor trained via meta learning that generalizes across classification/detection tasks, ResNet/ViT based architectures, and random/pre-trained initializations. The PPL improves the performance estimation on average by 37% across 16 classification datasets and 33% across 10 detection datasets, compared to the power law. We further extend the PPL to provide a confidence bound and use it to limit the prediction horizon that reduces over-estimation of data by 76% on classification and 91% on detection datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_A_Meta-Learning_Approach_to_Predicting_Performance_and_Data_Requirements_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_A_Meta-Learning_Approach_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01598 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_A_Meta-Learning_Approach_to_Predicting_Performance_and_Data_Requirements_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_A_Meta-Learning_Approach_to_Predicting_Performance_and_Data_Requirements_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert | Jiadong Wang, Xinyuan Qian, Malu Zhang, Robby T. Tan, Haizhou Li | Talking face generation, also known as speech-to-lip generation, reconstructs facial motions concerning lips given coherent speech input. The previous studies revealed the importance of lip-speech synchronization and visual quality. Despite much progress, they hardly focus on the content of lip movements i.e., the visual intelligibility of the spoken words, which is an important aspect of generation quality. To address the problem, we propose using a lip-reading expert to improve the intelligibility of the generated lip regions by penalizing the incorrect generation results. Moreover, to compensate for data scarcity, we train the lip-reading expert in an audio-visual self-supervised manner. With a lip-reading expert, we propose a novel contrastive learning to enhance lip-speech synchronization, and a transformer to encode audio synchronically with video, while considering global temporal dependency of audio. For evaluation, we propose a new strategy with two different lip-reading experts to measure intelligibility of the generated videos. Rigorous experiments show that our proposal is superior to other State-of-the-art (SOTA) methods, such as Wav2Lip, in reading intelligibility i.e., over 38% Word Error Rate (WER) on LRS2 dataset and 27.8% accuracy on LRW dataset. We also achieve the SOTA performance in lip-speech synchronization and comparable performances in visual quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Seeing_What_You_Said_Talking_Face_Generation_Guided_by_a_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Seeing_What_You_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17480 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Seeing_What_You_Said_Talking_Face_Generation_Guided_by_a_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Seeing_What_You_Said_Talking_Face_Generation_Guided_by_a_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model | Takehiro Aoshima, Takashi Matsubara | Semantic editing of images is the fundamental goal of computer vision. Although deep learning methods, such as generative adversarial networks (GANs), are capable of producing high-quality images, they often do not have an inherent way of editing generated images semantically. Recent studies have investigated a way of manipulating the latent variable to determine the images to be generated. However, methods that assume linear semantic arithmetic have certain limitations in terms of the quality of image editing, whereas methods that discover nonlinear semantic pathways provide non-commutative editing, which is inconsistent when applied in different orders. This study proposes a novel method called deep curvilinear editing (DeCurvEd) to determine semantic commuting vector fields on the latent space. We theoretically demonstrate that owing to commutativity, the editing of multiple attributes depends only on the quantities and not on the order. Furthermore, we experimentally demonstrate that compared to previous methods, the nonlinear and commutative nature of DeCurvEd provides higher-quality editing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Aoshima_Deep_Curvilinear_Editing_Commutative_and_Nonlinear_Image_Manipulation_for_Pretrained_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Aoshima_Deep_Curvilinear_Editing_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.14573 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Aoshima_Deep_Curvilinear_Editing_Commutative_and_Nonlinear_Image_Manipulation_for_Pretrained_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Aoshima_Deep_Curvilinear_Editing_Commutative_and_Nonlinear_Image_Manipulation_for_Pretrained_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement | Yuhui Wu, Chen Pan, Guoqing Wang, Yang Yang, Jiwei Wei, Chongyi Li, Heng Tao Shen | Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Learning_Semantic-Aware_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.07039 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.html | CVPR 2023 | null |
SimpSON: Simplifying Photo Cleanup With Single-Click Distracting Object Segmentation Network | Chuong Huynh, Yuqian Zhou, Zhe Lin, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi, Abhinav Shrivastava | In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removing these small and dense distracting regions can be a laborious and time-consuming task. In this paper, we propose an interactive distractor selection method that is optimized to achieve the task with just a single click. Our method surpasses the precision and recall achieved by the traditional method of running panoptic segmentation and then selecting the segments containing the clicks. We also showcase how a transformer-based module can be used to identify more distracting regions similar to the user's click position. Our experiments demonstrate that the model can effectively and accurately segment unknown distracting objects interactively and in groups. By significantly simplifying the photo cleaning and retouching process, our proposed model provides inspiration for exploring rare object segmentation and group selection with a single click. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huynh_SimpSON_Simplifying_Photo_Cleanup_With_Single-Click_Distracting_Object_Segmentation_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huynh_SimpSON_Simplifying_Photo_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huynh_SimpSON_Simplifying_Photo_Cleanup_With_Single-Click_Distracting_Object_Segmentation_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huynh_SimpSON_Simplifying_Photo_Cleanup_With_Single-Click_Distracting_Object_Segmentation_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Neural Duplex Radiance Fields for Real-Time View Synthesis | Ziyu Wan, Christian Richardt, Aljaž Božič, Chao Li, Vijay Rengarajan, Seonghyeon Nam, Xiaoyu Xiang, Tuotuo Li, Bo Zhu, Rakesh Ranjan, Jing Liao | Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations -- for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations that are fully compatible with the massively parallel graphics rendering pipeline. We represent scenes as neural radiance features encoded on a two-layer duplex mesh, which effectively overcomes the inherent inaccuracies in 3D surface reconstruction by learning the aggregated radiance information from a reliable interval of ray-surface intersections. To exploit local geometric relationships of nearby pixels, we leverage screen-space convolutions instead of the MLPs used in NeRFs to achieve high-quality appearance. Finally, the performance of the whole framework is further boosted by a novel multi-view distillation optimization strategy. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wan_Learning_Neural_Duplex_Radiance_Fields_for_Real-Time_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wan_Learning_Neural_Duplex_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wan_Learning_Neural_Duplex_Radiance_Fields_for_Real-Time_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wan_Learning_Neural_Duplex_Radiance_Fields_for_Real-Time_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance Pursuit | Xiaohang Wang, Xuanhong Chen, Bingbing Ni, Hang Wang, Zhengyan Tong, Yutian Liu | The ability of scale-equivariance processing blocks plays a central role in arbitrary-scale image super-resolution tasks. Inspired by this crucial observation, this work proposes two novel scale-equivariant modules within a transformer-style framework to enhance arbitrary-scale image super-resolution (ASISR) performance, especially in high upsampling rate image extrapolation. In the feature extraction phase, we design a plug-in module called Adaptive Feature Extractor, which injects explicit scale information in frequency-expanded encoding, thus achieving scale-adaption in representation learning. In the upsampling phase, a learnable Neural Kriging upsampling operator is introduced, which simultaneously encodes both relative distance (i.e., scale-aware) information as well as feature similarity (i.e., with priori learned from training data) in a bilateral manner, providing scale-encoded spatial feature fusion. The above operators are easily plugged into multiple stages of a SR network, and a recent emerging pre-training strategy is also adopted to impulse the model's performance further. Extensive experimental results have demonstrated the outstanding scale-equivariance capability offered by the proposed operators and our learning framework, with much better results than previous SOTAs at arbitrary scales for SR. Our code is available at https://github.com/neuralchen/EQSR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Deep_Arbitrary-Scale_Image_Super-Resolution_via_Scale-Equivariance_Pursuit_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Deep_Arbitrary-Scale_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Arbitrary-Scale_Image_Super-Resolution_via_Scale-Equivariance_Pursuit_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Arbitrary-Scale_Image_Super-Resolution_via_Scale-Equivariance_Pursuit_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Modality-Agnostic Person Re-Identification With Descriptive Query | Cuiqun Chen, Mang Ye, Ding Jiang | Person re-identification (ReID) with descriptive query (text or sketch) provides an important supplement for general image-image paradigms, which is usually studied in a single cross-modality matching manner, e.g., text-to-image or sketch-to-photo. However, without a camera-captured photo query, it is uncertain whether the text or sketch is available or not in practical scenarios. This motivates us to study a new and challenging modality-agnostic person re-identification problem. Towards this goal, we propose a unified person re-identification (UNIReID) architecture that can effectively adapt to cross-modality and multi-modality tasks. Specifically, UNIReID incorporates a simple dual-encoder with task-specific modality learning to mine and fuse visual and textual modality information. To deal with the imbalanced training problem of different tasks in UNIReID, we propose a task-aware dynamic training strategy in terms of task difficulty, adaptively adjusting the training focus. Besides, we construct three multi-modal ReID datasets by collecting the corresponding sketches from photos to support this challenging task. The experimental results on three multi-modal ReID datasets show that our UNIReID greatly improves the retrieval accuracy and generalization ability on different tasks and unseen scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Towards_Modality-Agnostic_Person_Re-Identification_With_Descriptive_Query_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Towards_Modality-Agnostic_Person_Re-Identification_With_Descriptive_Query_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Towards_Modality-Agnostic_Person_Re-Identification_With_Descriptive_Query_CVPR_2023_paper.html | CVPR 2023 | null |
Discriminating Known From Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder | Aming Wu, Cheng Deng | Discriminating known from unknown objects is an important essential ability for human beings. To simulate this ability, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect the objects that are never-seen-before during model training, which is beneficial for promoting the safe deployment of object detectors. Due to lacking unknown data for supervision, for this task, the main challenge lies in how to leverage the known in-distribution (ID) data to improve the detector's discrimination ability. In this paper, we first propose a method of Structure-Enhanced Recurrent Variational AutoEncoder (SR-VAE), which mainly consists of two dedicated recurrent VAE branches. Specifically, to boost the performance of object localization, we explore utilizing the classical Laplacian of Gaussian (LoG) operator to enhance the structure information in the extracted low-level features. Meanwhile, we design a VAE branch that recurrently generates the augmentation of the classification features to strengthen the discrimination ability of the object classifier. Finally, to alleviate the impact of lacking unknown data, another cycle-consistent conditional VAE branch is proposed to synthesize virtual OOD features that deviate from the distribution of ID features, which improves the capability of distinguishing OOD objects. In the experiments, our method is evaluated on OOD-OD, open-vocabulary detection, and incremental object detection. The significant performance gains over baselines show the superiorities of our method. The code will be released at https://github.com/AmingWu/SR-VAE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Discriminating_Known_From_Unknown_Objects_via_Structure-Enhanced_Recurrent_Variational_AutoEncoder_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Discriminating_Known_From_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Discriminating_Known_From_Unknown_Objects_via_Structure-Enhanced_Recurrent_Variational_AutoEncoder_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Discriminating_Known_From_Unknown_Objects_via_Structure-Enhanced_Recurrent_Variational_AutoEncoder_CVPR_2023_paper.html | CVPR 2023 | null |
Occlusion-Free Scene Recovery via Neural Radiance Fields | Chengxuan Zhu, Renjie Wan, Yunkai Tang, Boxin Shi | Our everyday lives are filled with occlusions that we strive to see through. By aggregating desired background information from different viewpoints, we can easily eliminate such occlusions without any external occlusion-free supervision. Though several occlusion removal methods have been proposed to empower machine vision systems with such ability, their performances are still unsatisfactory due to reliance on external supervision. We propose a novel method for occlusion removal by directly building a mapping between position and viewing angles and the corresponding occlusion-free scene details leveraging Neural Radiance Fields (NeRF). We also develop an effective scheme to jointly optimize camera parameters and scene reconstruction when occlusions are present. An additional depth constraint is applied to supervise the entire optimization without labeled external data for training. The experimental results on existing and newly collected datasets validate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Occlusion-Free_Scene_Recovery_via_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Occlusion-Free_Scene_Recovery_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Occlusion-Free_Scene_Recovery_via_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Occlusion-Free_Scene_Recovery_via_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization | Ying Zhao | Unsupervised anomaly localization and detection is crucial for industrial manufacturing processes due to the lack of anomalous samples. Recent unsupervised advances on industrial anomaly detection achieve high performance by training separate models for many different categories. The model storage and training time cost of this paradigm is high. Moreover, the setting of one-model-N-classes leads to fearful degradation of existing methods. In this paper, we propose a unified CNN framework for unsupervised anomaly localization, named OmniAL. This method conquers aforementioned problems by improving anomaly synthesis, reconstruction and localization. To prevent the model learning identical reconstruction, it trains the model with proposed panel-guided synthetic anomaly data rather than directly using normal data. It increases anomaly reconstruction error for multi-class distribution by using a network that is equipped with proposed Dilated Channel and Spatial Attention (DCSA) blocks. To better localize the anomaly regions, it employs proposed DiffNeck between reconstruction and localization sub-networks to explore multi-level differences. Experiments on 15-class MVTecAD and 12-class VisA datasets verify the advantage of proposed OmniAL that surpasses the state-of-the-art of unified models. On 15-class-MVTecAD/12-class-VisA, its single unified model achieves 97.2/87.8 image-AUROC, 98.3/96.6 pixel-AUROC and 73.4/41.7 pixel-AP for anomaly detection and localization respectively. Besides that, we make the first attempt to conduct a comprehensive study on the robustness of unsupervised anomaly localization and detection methods against different level adversarial attacks. Experiential results show OmniAL has good application prospects for its superior performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_OmniAL_A_Unified_CNN_Framework_for_Unsupervised_Anomaly_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_OmniAL_A_Unified_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_OmniAL_A_Unified_CNN_Framework_for_Unsupervised_Anomaly_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_OmniAL_A_Unified_CNN_Framework_for_Unsupervised_Anomaly_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions | Weijia Li, Saihui Hou, Chunjie Zhang, Chunshui Cao, Xu Liu, Yongzhen Huang, Yao Zhao | The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions. To tackle this problem, we propose a Cloth-Changing benchmark for Person re-identification and Gait recognition (CCPG). It is a cloth-changing dataset, and there are several highlights in CCPG, (1) it provides 200 identities and over 16K sequences are captured indoors and outdoors, (2) each identity has seven different cloth-changing statuses, which is hardly seen in previous datasets, (3) RGB and silhouettes version data are both available for research purposes. Moreover, aiming to investigate the cloth-changing problem systematically, comprehensive experiments are conducted on video-based ReID and gait recognition methods. The experimental results demonstrate the superiority of ReID and gait recognition separately in different cloth-changing conditions and suggest that gait recognition is a potential solution for addressing the cloth-changing problem. Our dataset will be available at https://github.com/BNU-IVC/CCPG. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving | Xiwen Liang, Minzhe Niu, Jianhua Han, Hang Xu, Chunjing Xu, Xiaodan Liang | Multi-task learning has emerged as a powerful paradigm to solve a range of tasks simultaneously with good efficiency in both computation resources and inference time. However, these algorithms are designed for different tasks mostly not within the scope of autonomous driving, thus making it hard to compare multi-task methods in autonomous driving. Aiming to enable the comprehensive evaluation of present multi-task learning methods in autonomous driving, we extensively investigate the performance of popular multi-task methods on the large-scale driving dataset, which covers four common perception tasks, i.e., object detection, semantic segmentation, drivable area segmentation, and lane detection. We provide an in-depth analysis of current multi-task learning methods under different common settings and find out that the existing methods make progress but there is still a large performance gap compared with single-task baselines. To alleviate this dilemma in autonomous driving, we present an effective multi-task framework, VE-Prompt, which introduces visual exemplars via task-specific prompting to guide the model toward learning high-quality task-specific representations. Specifically, we generate visual exemplars based on bounding boxes and color-based markers, which provide accurate visual appearances of target categories and further mitigate the performance gap. Furthermore, we bridge transformer-based encoders and convolutional layers for efficient and accurate unified perception in autonomous driving. Comprehensive experimental results on the diverse self-driving dataset BDD100K show that the VE-Prompt improves the multi-task baseline and further surpasses single-task models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_Visual_Exemplar_Driven_Task-Prompting_for_Unified_Perception_in_Autonomous_Driving_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liang_Visual_Exemplar_Driven_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Visual_Exemplar_Driven_Task-Prompting_for_Unified_Perception_in_Autonomous_Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Visual_Exemplar_Driven_Task-Prompting_for_Unified_Perception_in_Autonomous_Driving_CVPR_2023_paper.html | CVPR 2023 | null |
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation | Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin’ichi Satoh | Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images. However, our survey of 37 recent papers reveals that many works rely solely on automatic measures (e.g., FID) or perform poorly described human evaluations that are not reliable or repeatable. This paper proposes a standardized and well-defined human evaluation protocol to facilitate verifiable and reproducible human evaluation in future works. In our pilot data collection, we experimentally show that the current automatic measures are incompatible with human perception in evaluating the performance of the text-to-image generation results. Furthermore, we provide insights for designing human evaluation experiments reliably and conclusively. Finally, we make several resources publicly available to the community to facilitate easy and fast implementations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Otani_Toward_Verifiable_and_Reproducible_Human_Evaluation_for_Text-to-Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Otani_Toward_Verifiable_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01816 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Otani_Toward_Verifiable_and_Reproducible_Human_Evaluation_for_Text-to-Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Otani_Toward_Verifiable_and_Reproducible_Human_Evaluation_for_Text-to-Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised Domain Adaptation With Source Label Adaptation | Yu-Chu Yu, Hsuan-Tien Lin | Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen target data with a few labeled and lots of unlabeled target data, along with many labeled source data from a related domain. Current SSDA approaches usually aim at aligning the target data to the labeled source data with feature space mapping and pseudo-label assignments. Nevertheless, such a source-oriented model can sometimes align the target data to source data of the wrong classes, degrading the classification performance. This paper presents a novel source-adaptive paradigm that adapts the source data to match the target data. Our key idea is to view the source data as a noisily-labeled version of the ideal target data. Then, we propose an SSDA model that cleans up the label noise dynamically with the help of a robust cleaner component designed from the target perspective. Since the paradigm is very different from the core ideas behind existing SSDA approaches, our proposed model can be easily coupled with them to improve their performance. Empirical results on two state-of-the-art SSDA approaches demonstrate that the proposed model effectively cleans up the noise within the source labels and exhibits superior performance over those approaches across benchmark datasets. Our code is available at https://github.com/chu0802/SLA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Semi-Supervised_Domain_Adaptation_With_Source_Label_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Semi-Supervised_Domain_Adaptation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.02335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Semi-Supervised_Domain_Adaptation_With_Source_Label_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Semi-Supervised_Domain_Adaptation_With_Source_Label_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Range-Nullspace Video Frame Interpolation With Focalized Motion Estimation | Zhiyang Yu, Yu Zhang, Dongqing Zou, Xijun Chen, Jimmy S. Ren, Shunqing Ren | Continuous-time video frame interpolation is a fundamental technique in computer vision for its flexibility in synthesizing motion trajectories and novel video frames at arbitrary intermediate time steps. Yet, how to infer accurate intermediate motion and synthesize high-quality video frames are two critical challenges. In this paper, we present a novel VFI framework with improved treatment for these challenges. To address the former, we propose focalized trajectory fitting, which performs confidence-aware motion trajectory estimation by learning to pay focus to reliable optical flow candidates while suppressing the outliers. The second is range-nullspace synthesis, a novel frame renderer cast as solving an ill-posed problem addressed by learning decoupled components in orthogonal subspaces. The proposed framework sets new records on 7 of 10 public VFI benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Range-Nullspace_Video_Frame_Interpolation_With_Focalized_Motion_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Range-Nullspace_Video_Frame_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Range-Nullspace_Video_Frame_Interpolation_With_Focalized_Motion_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Range-Nullspace_Video_Frame_Interpolation_With_Focalized_Motion_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
FlowGrad: Controlling the Output of Generative ODEs With Gradients | Xingchao Liu, Lemeng Wu, Shujian Zhang, Chengyue Gong, Wei Ping, Qiang Liu | Generative modeling with ordinary differential equations (ODEs) has achieved fantastic results on a variety of applications. Yet, few works have focused on controlling the generated content of a pre-trained ODE-based generative model. In this paper, we propose to optimize the output of ODE models according to a guidance function to achieve controllable generation. We point out that, the gradients can be efficiently back-propagated from the output to any intermediate time steps on the ODE trajectory, by decomposing the back-propagation and computing vector-Jacobian products. To further accelerate the computation of the back-propagation, we propose to use a non-uniform discretization to approximate the ODE trajectory, where we measure how straight the trajectory is and gather the straight parts into one discretization step. This allows us to save 90% of the back-propagation time with ignorable error. Our framework, named FlowGrad, outperforms the state-of-the-art baselines on text-guided image manipulation. Moreover, FlowGrad enables us to find global semantic directions in frozen ODE-based generative models that can be used to manipulate new images without extra optimization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_FlowGrad_Controlling_the_Output_of_Generative_ODEs_With_Gradients_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_FlowGrad_Controlling_the_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_FlowGrad_Controlling_the_Output_of_Generative_ODEs_With_Gradients_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_FlowGrad_Controlling_the_Output_of_Generative_ODEs_With_Gradients_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions | Yurui Zhu, Tianyu Wang, Xueyang Fu, Xuanyu Yang, Xin Guo, Jifeng Dai, Yu Qiao, Xiaowei Hu | Image restoration under multiple adverse weather conditions aims to remove weather-related artifacts by using the single set of network parameters. In this paper, we find that distorted images under different weather conditions contain general characteristics as well as their specific characteristics. Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features. The first training stage aims to learn the weather-general features by taking the images under various weather conditions as the inputs and outputting the coarsely restored results. The second training stage aims to learn to adaptively expand the specific parameters for each weather type in the deep model, where requisite positions for expansion of weather-specific parameters are learned automatically. Hence, we can obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Moreover, we build the first real-world benchmark dataset with multiple weather conditions to better deal with real-world weather scenarios. Experimental results show that our method achieves superior performance on all the synthetic and real-world benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Learning_Weather-General_and_Weather-Specific_Features_for_Image_Restoration_Under_Multiple_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Learning_Weather-General_and_Weather-Specific_Features_for_Image_Restoration_Under_Multiple_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Learning_Weather-General_and_Weather-Specific_Features_for_Image_Restoration_Under_Multiple_CVPR_2023_paper.html | CVPR 2023 | null |
Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process | Yuhan Li, Yishun Dou, Xuanhong Chen, Bingbing Ni, Yilin Sun, Yutian Liu, Fuzhen Wang | We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ-VAE) is utilized to index local geometry from a compactly learned codebook based on a broad set of task training data. On the other hand, a discrete diffusion generator is introduced to model the inherent structural dependencies among different tokens. In the meantime, a multi-frequency fusion module (MFM) is developed to suppress high-frequency shape feature fluctuations, guided by multi-frequency contextual information. The above designs jointly equip our proposed 3D shape prior model with high-fidelity, diverse features as well as the capability of cross-modality alignment, and extensive experiments have demonstrated superior performances on various 3D shape generation tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Generalized_Deep_3D_Shape_Prior_via_Part-Discretized_Diffusion_Process_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Generalized_Deep_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Generalized_Deep_3D_Shape_Prior_via_Part-Discretized_Diffusion_Process_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Generalized_Deep_3D_Shape_Prior_via_Part-Discretized_Diffusion_Process_CVPR_2023_paper.html | CVPR 2023 | null |
Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation | Zicheng Wang, Zhen Zhao, Xiaoxia Xing, Dong Xu, Xiangyu Kong, Luping Zhou | Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based SSS methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new CCVC approach on the SSS benchmark datasets where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/CCVC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Conflict-Based_Cross-View_Consistency_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.01276 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Conflict-Based_Cross-View_Consistency_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Conflict-Based_Cross-View_Consistency_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning a 3D Morphable Face Reflectance Model From Low-Cost Data | Yuxuan Han, Zhibo Wang, Feng Xu | Modeling non-Lambertian effects such as facial specularity leads to a more realistic 3D Morphable Face Model. Existing works build parametric models for diffuse and specular albedo using Light Stage data. However, only diffuse and specular albedo cannot determine the full BRDF. In addition, the requirement of Light Stage data is hard to fulfill for the research communities. This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data. We apply linear shiness weighting into parametric modeling to represent spatially varying specular intensity and shiness. Then an inverse rendering algorithm is developed to reconstruct the reflectance parameters from non-Light Stage data, which are used to train an initial morphable reflectance model. To enhance the model's generalization capability and expressive power, we further propose an update-by-reconstruction strategy to finetune it on an in-the-wild dataset. Experimental results show that our method obtains decent rendering results with plausible facial specularities. Our code is released at https://yxuhan.github.io/ReflectanceMM/index.html. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_Learning_a_3D_Morphable_Face_Reflectance_Model_From_Low-Cost_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_Learning_a_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11686 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Learning_a_3D_Morphable_Face_Reflectance_Model_From_Low-Cost_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Learning_a_3D_Morphable_Face_Reflectance_Model_From_Low-Cost_Data_CVPR_2023_paper.html | CVPR 2023 | null |
SCoDA: Domain Adaptive Shape Completion for Real Scans | Yushuang Wu, Zizheng Yan, Ce Chen, Lai Wei, Xiao Li, Guanbin Li, Yihao Li, Shuguang Cui, Xiaoguang Han | 3D shape completion from point clouds is a challenging task, especially from scans of real-world objects. Considering the paucity of 3D shape ground truths for real scans, existing works mainly focus on benchmarking this task on synthetic data, e.g. 3D computer-aided design models. However, the domain gap between synthetic and real data limits the generalizability of these methods. Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data. A new dataset, ScanSalon, is contributed with a bunch of elaborate 3D models created by skillful artists according to scans. To address this new task, we propose a novel cross-domain feature fusion method for knowledge transfer and a novel volume-consistent self-training framework for robust learning from real data. Extensive experiments prove our method is effective to bring an improvement of 6% 7% mIoU. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_SCoDA_Domain_Adaptive_Shape_Completion_for_Real_Scans_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_SCoDA_Domain_Adaptive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10179 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_SCoDA_Domain_Adaptive_Shape_Completion_for_Real_Scans_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_SCoDA_Domain_Adaptive_Shape_Completion_for_Real_Scans_CVPR_2023_paper.html | CVPR 2023 | null |
Recurrent Homography Estimation Using Homography-Guided Image Warping and Focus Transformer | Si-Yuan Cao, Runmin Zhang, Lun Luo, Beinan Yu, Zehua Sheng, Junwei Li, Hui-Liang Shen | We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the attention-focusing mechanism in FocusFormer aggregates the intra-inter correspondence in a global->nonlocal->local manner. Thanks to the above strategies, RHWF ranks top in accuracy on a variety of datasets, including the challenging cross-resolution and cross-modal ones. Meanwhile, benefiting from the recurrent framework, RHWF achieves parameter efficiency despite the transformer architecture. Compared to previous state-of-the-art approaches LocalTrans and IHN, RHWF reduces the mean average corner error (MACE) by about 70% and 38.1% on the MSCOCO dataset, while saving the parameter costs by 86.5% and 24.6%. Similar to the previous works, RHWF can also be arranged in 1-scale for efficiency and 2-scale for accuracy, with the 1-scale RHWF already outperforming most of the previous methods. Source code is available at https://github.com/imdumpl78/RHWF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Recurrent_Homography_Estimation_Using_Homography-Guided_Image_Warping_and_Focus_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Recurrent_Homography_Estimation_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Recurrent_Homography_Estimation_Using_Homography-Guided_Image_Warping_and_Focus_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Recurrent_Homography_Estimation_Using_Homography-Guided_Image_Warping_and_Focus_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs | Jingsen Zhu, Yuchi Huo, Qi Ye, Fujun Luan, Jifan Li, Dianbing Xi, Lisha Wang, Rui Tang, Wei Hua, Hujun Bao, Rui Wang | In this work, we present I^2-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs). Our holistic neural SDF-based framework jointly recovers the underlying shapes, incident radiance and materials from multi-view images. We introduce a novel bubble loss for fine-grained small objects and error-guided adaptive sampling scheme to largely improve the reconstruction quality on large-scale indoor scenes. Further, we propose to decompose the neural radiance field into spatially-varying material of the scene as a neural field through surface-based, differentiable Monte Carlo raytracing and emitter semantic segmentations, which enables physically based and photorealistic scene relighting and editing applications. Through a number of qualitative and quantitative experiments, we demonstrate the superior quality of our method on indoor scene reconstruction, novel view synthesis, and scene editing compared to state-of-the-art baselines. Our project page is at https://jingsenzhu.github.io/i2-sdf. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_I2-SDF_Intrinsic_Indoor_Scene_Reconstruction_and_Editing_via_Raytracing_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_I2-SDF_Intrinsic_Indoor_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_I2-SDF_Intrinsic_Indoor_Scene_Reconstruction_and_Editing_via_Raytracing_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_I2-SDF_Intrinsic_Indoor_Scene_Reconstruction_and_Editing_via_Raytracing_in_CVPR_2023_paper.html | CVPR 2023 | null |
DLBD: A Self-Supervised Direct-Learned Binary Descriptor | Bin Xiao, Yang Hu, Bo Liu, Xiuli Bi, Weisheng Li, Xinbo Gao | For learning-based binary descriptors, the binarization process has not been well addressed. The reason is that the binarization blocks gradient back-propagation. Existing learning-based binary descriptors learn real-valued output, and then it is converted to binary descriptors by their proposed binarization processes. Since their binarization processes are not a component of the network, the learning-based binary descriptor cannot fully utilize the advances of deep learning. To solve this issue, we propose a model-agnostic plugin binary transformation layer (BTL), making the network directly generate binary descriptors. Then, we present the first self-supervised, direct-learned binary descriptor, dubbed DLBD. Furthermore, we propose ultra-wide temperature-scaled cross-entropy loss to adjust the distribution of learned descriptors in a larger range. Experiments demonstrate that the proposed BTL can substitute the previous binarization process. Our proposed DLBD outperforms SOTA on different tasks such as image retrieval and classification. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_DLBD_A_Self-Supervised_Direct-Learned_Binary_Descriptor_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_DLBD_A_Self-Supervised_Direct-Learned_Binary_Descriptor_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_DLBD_A_Self-Supervised_Direct-Learned_Binary_Descriptor_CVPR_2023_paper.html | CVPR 2023 | null |
Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation | Pengchong Qiao, Zhidan Wei, Yu Wang, Zhennan Wang, Guoli Song, Fan Xu, Xiangyang Ji, Chang Liu, Jie Chen | Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach. Codes are available in https://github.com/qpc1611094/FPL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qiao_Fuzzy_Positive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qiao_Fuzzy_Positive_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.08519 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qiao_Fuzzy_Positive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qiao_Fuzzy_Positive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields | Rohith Agaram, Shaurya Dewan, Rahul Sajnani, Adrien Poulenard, Madhava Krishna, Srinath Sridhar | Coordinate-based implicit neural networks, or neural fields, have emerged as useful representations of shape and appearance in 3D computer vision. Despite advances however, it remains challenging to build neural fields for categories of objects without datasets like ShapeNet that provide "canonicalized" object instances that are consistently aligned for their 3D position and orientation (pose). We present Canonical Field Network (CaFi-Net), a self-supervised method to canonicalize the 3D pose of instances from an object category represented as neural fields, specifically neural radiance fields (NeRFs). CaFi-Net directly learns from continuous and noisy radiance fields using a Siamese network architecture that is designed to extract equivariant field features for category-level canonicalization. During inference, our method takes pre-trained neural radiance fields of novel object instances at arbitrary 3D pose, and estimates a canonical field with consistent 3D pose across the entire category. Extensive experiments on a new dataset of 1300 NeRF models across 13 object categories show that our method matches or exceeds the performance of 3D point cloud-based methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Agaram_Canonical_Fields_Self-Supervised_Learning_of_Pose-Canonicalized_Neural_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Agaram_Canonical_Fields_Self-Supervised_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Agaram_Canonical_Fields_Self-Supervised_Learning_of_Pose-Canonicalized_Neural_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Agaram_Canonical_Fields_Self-Supervised_Learning_of_Pose-Canonicalized_Neural_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
TransFlow: Transformer As Flow Learner | Yawen Lu, Qifan Wang, Siqi Ma, Tong Geng, Yingjie Victor Chen, Huaijin Chen, Dongfang Liu | Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement. In this work, we propose TransFlow, a pure transformer architecture for optical flow estimation. Compared to dominant CNN-based methods, TransFlow demonstrates three advantages. First, it provides more accurate correlation and trustworthy matching in flow estimation by utilizing spatial self-attention and cross-attention mechanisms between adjacent frames to effectively capture global dependencies; Second, it recovers more compromised information (e.g., occlusion and motion blur) in flow estimation through long-range temporal association in dynamic scenes; Third, it enables a concise self-learning paradigm and effectively eliminate the complex and laborious multi-stage pre-training procedures. We achieve the state-of-the-art results on the Sintel, KITTI-15, as well as several downstream tasks, including video object detection, interpolation and stabilization. For its efficacy, we hope TransFlow could serve as a flexible baseline for optical flow estimation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_TransFlow_Transformer_As_Flow_Learner_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.11523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_TransFlow_Transformer_As_Flow_Learner_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_TransFlow_Transformer_As_Flow_Learner_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-View Inverse Rendering for Large-Scale Real-World Indoor Scenes | Zhen Li, Lingli Wang, Mofang Cheng, Cihui Pan, Jiaqi Yang | We present a efficient multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs. Unlike previous representations, where the global illumination of large scenes is simplified as multiple environment maps, we propose a compact representation called Texture-based Lighting (TBL). It consists of 3D mesh and HDR textures, and efficiently models direct and infinite-bounce indirect lighting of the entire large scene. Based on TBL, we further propose a hybrid lighting representation with precomputed irradiance, which significantly improves the efficiency and alleviates the rendering noise in the material optimization. To physically disentangle the ambiguity between materials, we propose a three-stage material optimization strategy based on the priors of semantic segmentation and room segmentation. Extensive experiments show that the proposed method outperforms the state-of-the-art quantitatively and qualitatively, and enables physically-reasonable mixed-reality applications such as material editing, editable novel view synthesis and relighting. The project page is at https://lzleejean.github.io/TexIR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Multi-View_Inverse_Rendering_for_Large-Scale_Real-World_Indoor_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Multi-View_Inverse_Rendering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10206 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Multi-View_Inverse_Rendering_for_Large-Scale_Real-World_Indoor_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Multi-View_Inverse_Rendering_for_Large-Scale_Real-World_Indoor_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
AutoFocusFormer: Image Segmentation off the Grid | Chen Ziwen, Kaushik Patnaik, Shuangfei Zhai, Alvin Wan, Zhile Ren, Alexander G. Schwing, Alex Colburn, Li Fuxin | Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small objects are represented in very few spatial locations, leading to worse results in tasks such as segmentation. Intuitively, retaining more pixels representing small objects during downsampling helps to preserve important information. To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task. Since adaptive downsampling generates a set of pixels irregularly distributed on the image plane, we abandon the classic grid structure. Instead, we develop a novel point-based local attention block, facilitated by a balanced clustering module and a learnable neighborhood merging module, which yields representations for our point-based versions of state-of-the-art segmentation heads. Experiments show that our AutoFocusFormer (AFF) improves significantly over baseline models of similar sizes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ziwen_AutoFocusFormer_Image_Segmentation_off_the_Grid_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ziwen_AutoFocusFormer_Image_Segmentation_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.12406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ziwen_AutoFocusFormer_Image_Segmentation_off_the_Grid_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ziwen_AutoFocusFormer_Image_Segmentation_off_the_Grid_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Transductive Few-Shot Fine-Tuning With Margin-Based Uncertainty Weighting and Probability Regularization | Ran Tao, Hao Chen, Marios Savvides | Few-Shot Learning (FSL) has been rapidly developed in recent years, potentially eliminating the requirement for significant data acquisition. Few-shot fine-tuning has been demonstrated to be practically efficient and helpful, especially for out-of-distribution datum. In this work, we first observe that the few-shot fine-tuned methods are learned with the imbalanced class marginal distribution. This observation further motivates us to propose the Transductive Fine-tuning with Margin-based uncertainty weighting and Probability regularization (TF-MP), which learns a more balanced class marginal distribution. We first conduct sample weighting on the testing data with margin-based uncertainty scores and further regularize each test sample's categorical probability. TF-MP achieves state-of-the-art performance on in- / out-of-distribution evaluations of Meta-Dataset and surpasses previous transductive methods by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tao_Boosting_Transductive_Few-Shot_Fine-Tuning_With_Margin-Based_Uncertainty_Weighting_and_Probability_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tao_Boosting_Transductive_Few-Shot_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_Boosting_Transductive_Few-Shot_Fine-Tuning_With_Margin-Based_Uncertainty_Weighting_and_Probability_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_Boosting_Transductive_Few-Shot_Fine-Tuning_With_Margin-Based_Uncertainty_Weighting_and_Probability_CVPR_2023_paper.html | CVPR 2023 | null |
SMPConv: Self-Moving Point Representations for Continuous Convolution | Sanghyeon Kim, Eunbyung Park | Continuous convolution has recently gained prominence due to its ability to handle irregularly sampled data and model long-term dependency. Also, the promising experimental results of using large convolutional kernels have catalyzed the development of continuous convolution since they can construct large kernels very efficiently. Leveraging neural networks, more specifically multilayer perceptrons (MLPs), is by far the most prevalent approach to implementing continuous convolution. However, there are a few drawbacks, such as high computational costs, complex hyperparameter tuning, and limited descriptive power of filters. This paper suggests an alternative approach to building a continuous convolution without neural networks, resulting in more computationally efficient and improved performance. We present self-moving point representations where weight parameters freely move, and interpolation schemes are used to implement continuous functions. When applied to construct convolutional kernels, the experimental results have shown improved performance with drop-in replacement in the existing frameworks. Due to its lightweight structure, we are first to demonstrate the effectiveness of continuous convolution in a large-scale setting, e.g., ImageNet, presenting the improvements over the prior arts. Our code is available on https://github.com/sangnekim/SMPConv | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_SMPConv_Self-Moving_Point_Representations_for_Continuous_Convolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_SMPConv_Self-Moving_Point_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02330 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_SMPConv_Self-Moving_Point_Representations_for_Continuous_Convolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_SMPConv_Self-Moving_Point_Representations_for_Continuous_Convolution_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP2Protect: Protecting Facial Privacy Using Text-Guided Makeup via Adversarial Latent Search | Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar | The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate naturalistic' images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the given image from its latent code. This step produces a good initialization, aiding the generation of high-quality faces that resemble the given identity. Subsequently, user defined makeup text prompts and identity-preserving regularization are used to guide the search for adversarial codes in the latent space. Extensive experiments demonstrate that faces generated by our approach have stronger black-box transferability with an absolute gain of 12.06% over the state-of-the-art facial privacy protection approach under the face verification task. Finally, we demonstrate the effectiveness of the proposed approach for commercial face recognition systems. Our code is available at https://github.com/fahadshamshad/Clip2Protect. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shamshad_CLIP2Protect_Protecting_Facial_Privacy_Using_Text-Guided_Makeup_via_Adversarial_Latent_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shamshad_CLIP2Protect_Protecting_Facial_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shamshad_CLIP2Protect_Protecting_Facial_Privacy_Using_Text-Guided_Makeup_via_Adversarial_Latent_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shamshad_CLIP2Protect_Protecting_Facial_Privacy_Using_Text-Guided_Makeup_via_Adversarial_Latent_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels | Jingqiu Zhou, Linjiang Huang, Liang Wang, Si Liu, Hongsheng Li | The task of weakly supervised temporal action localization targets at generating temporal boundaries for actions of interest, meanwhile the action category should also be classified. Pseudo-label-based methods, which serve as an effective solution, have been widely studied recently. However, existing methods generate pseudo labels during training and make predictions during testing under different pipelines or settings, resulting in a gap between training and testing. In this paper, we propose to generate high-quality pseudo labels from the predicted action boundaries. Nevertheless, we note that existing post-processing, like NMS, would lead to information loss, which is insufficient to generate high-quality action boundaries. More importantly, transforming action boundaries into pseudo labels is quite challenging, since the predicted action instances are generally overlapped and have different confidence scores. Besides, the generated pseudo-labels can be fluctuating and inaccurate at the early stage of training. It might repeatedly strengthen the false predictions if there is no mechanism to conduct self-correction. To tackle these issues, we come up with an effective pipeline for learning better pseudo labels. Firstly, we propose a Gaussian weighted fusion module to preserve information of action instances and obtain high-quality action boundaries. Second, we formulate the pseudo-label generation as an optimization problem under the constraints in terms of the confidence scores of action instances. Finally, we introduce the idea of Delta pseudo labels, which enables the model with the ability of self-correction. Our method achieves superior performance to existing methods on two benchmarks, THUMOS14 and ActivityNet1.3, achieving gains of 1.9% on THUMOS14 and 3.7% on ActivityNet1.3 in terms of average mAP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Improving_Weakly_Supervised_Temporal_Action_Localization_by_Bridging_Train-Test_Gap_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Improving_Weakly_Supervised_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.07978 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Improving_Weakly_Supervised_Temporal_Action_Localization_by_Bridging_Train-Test_Gap_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Improving_Weakly_Supervised_Temporal_Action_Localization_by_Bridging_Train-Test_Gap_CVPR_2023_paper.html | CVPR 2023 | null |
PRISE: Demystifying Deep Lucas-Kanade With Strongly Star-Convex Constraints for Multimodel Image Alignment | Yiqing Zhang, Xinming Huang, Ziming Zhang | The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs have large distortions. To address this challenge, in this paper we propose a novel Deep Star-Convexified Lucas-Kanade (PRISE) method for multimodel image alignment by introducing strongly star-convex constraints into the optimization problem. Our basic idea is to enforce the neural network to approximately learn a star-convex loss landscape around the ground truth give any data to facilitate the convergence of the LK method to the ground truth through the high dimensional space defined by the network. This leads to a minimax learning problem, with contrastive (hinge) losses due to the definition of strong star-convexity that are appended to the original loss for training. We also provide an efficient sampling based algorithm to leverage the training cost, as well as some analysis on the quality of the solutions from PRISE. We further evaluate our approach on benchmark datasets such as MSCOCO, GoogleEarth, and GoogleMap, and demonstrate state-of-the-art results, especially for small pixel errors. Demo code is attached. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_PRISE_Demystifying_Deep_Lucas-Kanade_With_Strongly_Star-Convex_Constraints_for_Multimodel_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.11526 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PRISE_Demystifying_Deep_Lucas-Kanade_With_Strongly_Star-Convex_Constraints_for_Multimodel_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PRISE_Demystifying_Deep_Lucas-Kanade_With_Strongly_Star-Convex_Constraints_for_Multimodel_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing | Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Pérez-García, Maximilian Ilse, Daniel C. Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, Anton Schwaighofer, Maria Wetscherek, Matthew P. Lungren, Aditya Nori, Javier Alvarez-Valle, Ozan Oktay | Self-supervised learning in vision--language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN--Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, CXR-T, to quantify the quality of vision--language representations in terms of temporal semantics. Our experimental results show the significant advantages of incorporating prior images and reports to make most use of the data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bannur_Learning_To_Exploit_Temporal_Structure_for_Biomedical_Vision-Language_Processing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bannur_Learning_To_Exploit_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.04558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bannur_Learning_To_Exploit_Temporal_Structure_for_Biomedical_Vision-Language_Processing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bannur_Learning_To_Exploit_Temporal_Structure_for_Biomedical_Vision-Language_Processing_CVPR_2023_paper.html | CVPR 2023 | null |
Simple Cues Lead to a Strong Multi-Object Tracker | Jenny Seidenschwarz, Guillem Brasó, Víctor Castro Serrano, Ismail Elezi, Laura Leal-Taixé | For a long time, the most common paradigm in MultiObject Tracking was tracking-by-detection (TbD), where objects are first detected and then associated over video frames. For association, most models resourced to motion and appearance cues, e.g., re-identification networks. Recent approaches based on attention propose to learn the cues in a data-driven manner, showing impressive results. In this paper, we ask ourselves whether simple good old TbD methods are also capable of achieving the performance of end-to-end models. To this end, we propose two key ingredients that allow a standard re-identification network to excel at appearance-based tracking. We extensively analyse its failure cases, and show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our tracker generalizes to four public datasets, namely MOT17, MOT20, BDD100k, and DanceTrack, achieving state-ofthe-art performance. https://github.com/dvl-tum/GHOST | https://openaccess.thecvf.com/content/CVPR2023/papers/Seidenschwarz_Simple_Cues_Lead_to_a_Strong_Multi-Object_Tracker_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Seidenschwarz_Simple_Cues_Lead_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Seidenschwarz_Simple_Cues_Lead_to_a_Strong_Multi-Object_Tracker_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Seidenschwarz_Simple_Cues_Lead_to_a_Strong_Multi-Object_Tracker_CVPR_2023_paper.html | CVPR 2023 | null |
Marching-Primitives: Shape Abstraction From Signed Distance Function | Weixiao Liu, Yuwei Wu, Sipu Ruan, Gregory S. Chirikjian | Representing complex objects with basic geometric primitives has long been a topic in computer vision. Primitive-based representations have the merits of compactness and computational efficiency in higher-level tasks such as physics simulation, collision checking, and robotic manipulation. Unlike previous works which extract polygonal meshes from a signed distance function (SDF), in this paper, we present a novel method, named Marching-Primitives, to obtain a primitive-based abstraction directly from an SDF. Our method grows geometric primitives (such as superquadrics) iteratively by analyzing the connectivity of voxels while marching at different levels of signed distance. For each valid connected volume of interest, we march on the scope of voxels from which a primitive is able to be extracted in a probabilistic sense and simultaneously solve for the parameters of the primitive to capture the underlying local geometry. We evaluate the performance of our method on both synthetic and real-world datasets. The results show that the proposed method outperforms the state-of-the-art in terms of accuracy, and is directly generalizable among different categories and scales. The code is open-sourced at https://github.com/ChirikjianLab/Marching-Primitives.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Marching-Primitives_Shape_Abstraction_From_Signed_Distance_Function_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Marching-Primitives_Shape_Abstraction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Marching-Primitives_Shape_Abstraction_From_Signed_Distance_Function_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Marching-Primitives_Shape_Abstraction_From_Signed_Distance_Function_CVPR_2023_paper.html | CVPR 2023 | null |
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing | Jongin Lim, Youngdong Kim, Byungjai Kim, Chanho Ahn, Jinwoo Shin, Eunho Yang, Seungju Han | Neural networks are often prone to bias toward spurious correlations inherent in a dataset, thus failing to generalize unbiased test criteria. A key challenge to resolving the issue is the significant lack of bias-conflicting training data (i.e., samples without spurious correlations). In this paper, we propose a novel data augmentation approach termed Bias-Adversarial augmentation (BiasAdv) that supplements bias-conflicting samples with adversarial images. Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate synthetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model. Specifically, we formulate an optimization problem for generating adversarial images that attack the predictions of an auxiliary biased model without ruining the predictions of the desired debiased model. Despite its simplicity, we find that BiasAdv can generate surprisingly useful synthetic bias-conflicting samples, allowing the debiased model to learn generalizable representations. Furthermore, BiasAdv does not require any bias annotations or prior knowledge of the bias type, which enables its broad applicability to existing debiasing methods to improve their performances. Our extensive experimental results demonstrate the superiority of BiasAdv, achieving state-of-the-art performance on four popular benchmark datasets across various bias domains. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lim_BiasAdv_Bias-Adversarial_Augmentation_for_Model_Debiasing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lim_BiasAdv_Bias-Adversarial_Augmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lim_BiasAdv_Bias-Adversarial_Augmentation_for_Model_Debiasing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lim_BiasAdv_Bias-Adversarial_Augmentation_for_Model_Debiasing_CVPR_2023_paper.html | CVPR 2023 | null |
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion | Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Shuang Xu, Zudi Lin, Radu Timofte, Luc Van Gool | Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features uncorrelated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark. The code is available at https://github.com/Zhaozixiang1228/MMIF-CDDFuse. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_CDDFuse_Correlation-Driven_Dual-Branch_Feature_Decomposition_for_Multi-Modality_Image_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_CDDFuse_Correlation-Driven_Dual-Branch_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14461 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_CDDFuse_Correlation-Driven_Dual-Branch_Feature_Decomposition_for_Multi-Modality_Image_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_CDDFuse_Correlation-Driven_Dual-Branch_Feature_Decomposition_for_Multi-Modality_Image_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval | Ding Jiang, Mang Ye | Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. Prior works have attempted to address this challenge by leveraging separately pre-trained unimodal models to extract visual and textual features. However, these approaches lack the necessary underlying alignment capabilities required to match multimodal data effectively. Besides, these works use prior information to explore explicit part alignments, which may lead to the distortion of intra-modality information. To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision. Specifically, we first design an Implicit Relation Reasoning module in a masked language modeling paradigm. This achieves cross-modal interaction by integrating the visual cues into the textual tokens with a cross-modal multimodal interaction encoder. Secondly, to globally align the visual and textual embeddings, Similarity Distribution Matching is proposed to minimize the KL divergence between image-text similarity distributions and the normalized label matching distributions. The proposed method achieves new state-of-the-art results on all three public datasets, with a notable margin of about 3%-9% for Rank-1 accuracy compared to prior methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Cross-Modal_Implicit_Relation_Reasoning_and_Aligning_for_Text-to-Image_Person_Retrieval_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.12501 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Cross-Modal_Implicit_Relation_Reasoning_and_Aligning_for_Text-to-Image_Person_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Cross-Modal_Implicit_Relation_Reasoning_and_Aligning_for_Text-to-Image_Person_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
REVEAL: Retrieval-Augmented Visual-Language Pre-Training With Multi-Source Multimodal Knowledge Memory | Ziniu Hu, Ahmet Iscen, Chen Sun, Zirui Wang, Kai-Wei Chang, Yizhou Sun, Cordelia Schmid, David A. Ross, Alireza Fathi | In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc.) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_REVEAL_Retrieval-Augmented_Visual-Language_Pre-Training_With_Multi-Source_Multimodal_Knowledge_Memory_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_REVEAL_Retrieval-Augmented_Visual-Language_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_REVEAL_Retrieval-Augmented_Visual-Language_Pre-Training_With_Multi-Source_Multimodal_Knowledge_Memory_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_REVEAL_Retrieval-Augmented_Visual-Language_Pre-Training_With_Multi-Source_Multimodal_Knowledge_Memory_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Retain While Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation | Gaurav Patel, Konda Reddy Mopuri, Qiang Qiu | Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the fundamental idea of carrying out knowledge transfer from a Teacher neural network to a Student neural network in the absence of training data. However, in the Adversarial DFKD framework, the student network's accuracy, suffers due to the non-stationary distribution of the pseudo-samples under multiple generator updates. To this end, at every generator update, we aim to maintain the student's performance on previously encountered examples while acquiring knowledge from samples of the current distribution. Thus, we propose a meta-learning inspired framework by treating the task of Knowledge-Acquisition (learning from newly generated samples) and Knowledge-Retention (retaining knowledge on previously met samples) as meta-train and meta-test, respectively. Hence, we dub our method as Learning to Retain while Acquiring. Moreover, we identify an implicit aligning factor between the Knowledge-Retention and Knowledge-Acquisition tasks indicating that the proposed student update strategy enforces a common gradient direction for both tasks, alleviating interference between the two objectives. Finally, we support our hypothesis by exhibiting extensive evaluation and comparison of our method with prior arts on multiple datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Patel_Learning_To_Retain_While_Acquiring_Combating_Distribution-Shift_in_Adversarial_Data-Free_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Patel_Learning_To_Retain_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14290 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Patel_Learning_To_Retain_While_Acquiring_Combating_Distribution-Shift_in_Adversarial_Data-Free_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Patel_Learning_To_Retain_While_Acquiring_Combating_Distribution-Shift_in_Adversarial_Data-Free_CVPR_2023_paper.html | CVPR 2023 | null |
Why Is the Winner the Best? | Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu D. Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton D. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Plotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem A. Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen Yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein | International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. | https://openaccess.thecvf.com/content/CVPR2023/papers/Eisenmann_Why_Is_the_Winner_the_Best_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Eisenmann_Why_Is_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17719 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Eisenmann_Why_Is_the_Winner_the_Best_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Eisenmann_Why_Is_the_Winner_the_Best_CVPR_2023_paper.html | CVPR 2023 | null |
HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces | Ting Yao, Yehao Li, Yingwei Pan, Tao Mei | Parsing an unstructured point set into constituent local geometry structures (e.g., edges or surfaces) would be helpful for understanding and representing point clouds. This motivates us to design a deep architecture to model the hierarchical geometry from points, edges, surfaces (triangles), to super-surfaces (adjacent surfaces) for the thorough analysis of point clouds. In this paper, we present a novel Hierarchical Geometry Network (HGNet) that integrates such hierarchical geometry structures from super-surfaces, surfaces, edges, to points in a top-down manner for learning point cloud representations. Technically, we first construct the edges between every two neighbor points. A point-level representation is learnt with edge-to-point aggregation, i.e., aggregating all connected edges into the anchor point. Next, as every two neighbor edges compose a surface, we obtain the edge-level representation of each anchor edge via surface-to-edge aggregation over all neighbor surfaces. Furthermore, the surface-level representation is achieved through super-surface-to-surface aggregation by transforming all super-surfaces into the anchor surface. A Transformer structure is finally devised to unify all the point-level, edge-level, and surface-level features into the holistic point cloud representations. Extensive experiments on four point cloud analysis datasets demonstrate the superiority of HGNet for 3D object classification and part/semantic segmentation tasks. More remarkably, HGNet achieves the overall accuracy of 89.2% on ScanObjectNN, improving PointNeXt-S by 1.5%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_HGNet_Learning_Hierarchical_Geometry_From_Points_Edges_and_Surfaces_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_HGNet_Learning_Hierarchical_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_HGNet_Learning_Hierarchical_Geometry_From_Points_Edges_and_Surfaces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_HGNet_Learning_Hierarchical_Geometry_From_Points_Edges_and_Surfaces_CVPR_2023_paper.html | CVPR 2023 | null |
PointVector: A Vector Representation in Point Cloud Analysis | Xin Deng, WenYu Zhang, Qing Ding, XinMing Zhang | In point cloud analysis, point-based methods have rapidly developed in recent years. These methods have recently focused on concise MLP structures, such as PointNeXt, which have demonstrated competitiveness with Convolutional and Transformer structures. However, standard MLPs are limited in their ability to extract local features effectively. To address this limitation, we propose a Vector-oriented Point Set Abstraction that can aggregate neighboring features through higher-dimensional vectors. To facilitate network optimization, we construct a transformation from scalar to vector using independent angles based on 3D vector rotations. Finally, we develop a PointVector model that follows the structure of PointNeXt. Our experimental results demonstrate that PointVector achieves state-of-the-art performance 72.3% mIOU on the S3DIS Area 5 and 78.4% mIOU on the S3DIS (6-fold cross-validation) with only 58% model parameters of PointNeXt. We hope our work will help the exploration of concise and effective feature representations. The code will be released soon. | https://openaccess.thecvf.com/content/CVPR2023/papers/Deng_PointVector_A_Vector_Representation_in_Point_Cloud_Analysis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Deng_PointVector_A_Vector_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.10528 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_PointVector_A_Vector_Representation_in_Point_Cloud_Analysis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_PointVector_A_Vector_Representation_in_Point_Cloud_Analysis_CVPR_2023_paper.html | CVPR 2023 | null |
BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation | Cong Pan, Yonghao He, Junran Peng, Qian Zhang, Wei Sui, Zhaoxiang Zhang | Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multi-view image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_BAEFormer_Bi-Directional_and_Early_Interaction_Transformers_for_Birds_Eye_View_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_BAEFormer_Bi-Directional_and_Early_Interaction_Transformers_for_Birds_Eye_View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_BAEFormer_Bi-Directional_and_Early_Interaction_Transformers_for_Birds_Eye_View_CVPR_2023_paper.html | CVPR 2023 | null |
Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification | Zhengwei Yang, Meng Lin, Xian Zhong, Yu Wu, Zheng Wang | Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM, is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Good_Is_Bad_Causality_Inspired_Cloth-Debiasing_for_Cloth-Changing_Person_Re-Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Good_Is_Bad_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Good_Is_Bad_Causality_Inspired_Cloth-Debiasing_for_Cloth-Changing_Person_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Good_Is_Bad_Causality_Inspired_Cloth-Debiasing_for_Cloth-Changing_Person_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
Use Your Head: Improving Long-Tail Video Recognition | Toby Perrett, Saptarshi Sinha, Tilo Burghardt, Majid Mirmehdi, Dima Damen | This paper presents an investigation into long-tail video recognition. We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties. Most critically, they lack few-shot classes in their tails. In response, we propose new video benchmarks that better assess long-tail recognition, by sampling subsets from two datasets: SSv2 and VideoLT. We then propose a method, Long-Tail Mixed Reconstruction (LMR), which reduces overfitting to instances from few-shot classes by reconstructing them as weighted combinations of samples from head classes. LMR then employs label mixing to learn robust decision boundaries. It achieves state-of-the-art average class accuracy on EPIC-KITCHENS and the proposed SSv2-LT and VideoLT-LT. Benchmarks and code at: github.com/tobyperrett/lmr | https://openaccess.thecvf.com/content/CVPR2023/papers/Perrett_Use_Your_Head_Improving_Long-Tail_Video_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Perrett_Use_Your_Head_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Perrett_Use_Your_Head_Improving_Long-Tail_Video_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Perrett_Use_Your_Head_Improving_Long-Tail_Video_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting the P3P Problem | Yaqing Ding, Jian Yang, Viktor Larsson, Carl Olsson, Kalle Åström | One of the classical multi-view geometry problems is the so called P3P problem, where the absolute pose of a calibrated camera is determined from three 2D-to-3D correspondences. Since these solvers form a critical component of many vision systems (e.g. in localization and Structure-from-Motion), there have been significant effort in developing faster and more stable algorithms. While the current state-of-the-art solvers are both extremely fast and stable, there still exist configurations where they break down. In this paper we algebraically formulate the problem as finding the intersection of two conics. With this formulation we are able to analytically characterize the real roots of the polynomial system and employ a tailored solution strategy for each problem instance. The result is a fast and completely stable solver, that is able to correctly solve cases where competing methods fail. Our experimental evaluation shows that we outperform the current state-of-the-art methods both in terms of speed and success rate. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_Revisiting_the_P3P_Problem_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_Revisiting_the_P3P_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Revisiting_the_P3P_Problem_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Revisiting_the_P3P_Problem_CVPR_2023_paper.html | CVPR 2023 | null |
Generic-to-Specific Distillation of Masked Autoencoders | Wei Huang, Zhiliang Peng, Li Dong, Furu Wei, Jianbin Jiao, Qixiang Ye | Large vision Transformers (ViTs) driven by self-supervised pre-training mechanisms achieved unprecedented progress. Lightweight ViT models limited by the model capacity, however, benefit little from those pre-training mechanisms. Knowledge distillation defines a paradigm to transfer representations from large (teacher) models to small (student) ones. However, the conventional single-stage distillation easily gets stuck on task-specific transfer, failing to retain the task-agnostic knowledge crucial for model generalization. In this study, we propose generic-to-specific distillation (G2SD), to tap the potential of small ViT models under the supervision of large models pre-trained by masked autoencoders. In generic distillation, decoder of the small model is encouraged to align feature predictions with hidden representations of the large model, so that task-agnostic knowledge can be transferred. In specific distillation, predictions of the small model are constrained to be consistent with those of the large model, to transfer task-specific features which guarantee task performance. With G2SD, the vanilla ViT-Small model respectively achieves 98.7%, 98.1% and 99.3% the performance of its teacher (ViT-Base) for image classification, object detection, and semantic segmentation, setting a solid baseline for two-stage vision distillation. Code will be available at https://github.com/pengzhiliang/G2SD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Generic-to-Specific_Distillation_of_Masked_Autoencoders_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Generic-to-Specific_Distillation_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14771 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Generic-to-Specific_Distillation_of_Masked_Autoencoders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Generic-to-Specific_Distillation_of_Masked_Autoencoders_CVPR_2023_paper.html | CVPR 2023 | null |
PAniC-3D: Stylized Single-View 3D Reconstruction From Portraits of Anime Characters | Shuhong Chen, Kevin Zhang, Yichun Shi, Heng Wang, Yiheng Zhu, Guoxian Song, Sizhe An, Janus Kristjansson, Xiao Yang, Matthias Zwicker | We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challenges to single-view reconstruction; compared to natural images of human heads, character portrait illustrations have hair and accessories with more complex and diverse geometry, and are shaded with non-photorealistic contour lines. In addition, there is a lack of both 3D model and portrait illustration data suitable to train and evaluate this ambiguous stylized reconstruction task. Facing these challenges, our proposed PAniC-3D architecture crosses the illustration-to-3D domain gap with a line-filling model, and represents sophisticated geometries with a volumetric radiance field. We train our system with two large new datasets (11.2k Vroid 3D models, 1k Vtuber portrait illustrations), and evaluate on a novel AnimeRecon benchmark of illustration-to-3D pairs. PAniC-3D significantly outperforms baseline methods, and provides data to establish the task of stylized reconstruction from portrait illustrations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_PAniC-3D_Stylized_Single-View_3D_Reconstruction_From_Portraits_of_Anime_Characters_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_PAniC-3D_Stylized_Single-View_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_PAniC-3D_Stylized_Single-View_3D_Reconstruction_From_Portraits_of_Anime_Characters_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_PAniC-3D_Stylized_Single-View_3D_Reconstruction_From_Portraits_of_Anime_Characters_CVPR_2023_paper.html | CVPR 2023 | null |
Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation | Ruixuan Cong, Da Yang, Rongshan Chen, Sizhe Wang, Zhenglong Cui, Hao Sheng | Since light field simultaneously records spatial information and angular information of light rays, it is considered to be beneficial for many potential applications, and semantic segmentation is one of them. The regular variation of image information across views facilitates a comprehensive scene understanding. However, in the case of limited memory, the high-dimensional property of light field makes the problem more intractable than generic semantic segmentation, manifested in the difficulty of fully exploiting the relationships among views while maintaining contextual information in single view. In this paper, we propose a novel network called LF-IENet for light field semantic segmentation. It contains two different manners to mine complementary information from surrounding views to segment central view. One is implicit feature integration that leverages attention mechanism to compute inter-view and intra-view similarity to modulate features of central view. The other is explicit feature propagation that directly warps features of other views to central view under the guidance of disparity. They complement each other and jointly realize complementary information fusion across views in light field. The proposed method achieves outperforming performance on both real-world and synthetic light field datasets, demonstrating the effectiveness of this new architecture. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cong_Combining_Implicit-Explicit_View_Correlation_for_Light_Field_Semantic_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cong_Combining_Implicit-Explicit_View_Correlation_for_Light_Field_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cong_Combining_Implicit-Explicit_View_Correlation_for_Light_Field_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition | Ishan Rajendrakumar Dave, Mamshad Nayeem Rizve, Chen Chen, Mubarak Shah | Semi-Supervised Learning can be more beneficial for the video domain compared to images because of its higher annotation cost and dimensionality. Besides, any video understanding task requires reasoning over both spatial and temporal dimensions. In order to learn both the static and motion related features for the semi-supervised action recognition task, existing methods rely on hard input inductive biases like using two-modalities (RGB and Optical-flow) or two-stream of different playback rates. Instead of utilizing unlabeled videos through diverse input streams, we rely on self-supervised video representations, particularly, we utilize temporally-invariant and temporally-distinctive representations. We observe that these representations complement each other depending on the nature of the action. Based on this observation, we propose a student-teacher semi-supervised learning framework, TimeBalance, where we distill the knowledge from a temporally-invariant and a temporally-distinctive teacher. Depending on the nature of the unlabeled video, we dynamically combine the knowledge of these two teachers based on a novel temporal similarity-based reweighting scheme. Our method achieves state-of-the-art performance on three action recognition benchmarks: UCF101, HMDB51, and Kinetics400. Code: https://github.com/DAVEISHAN/TimeBalance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dave_TimeBalance_Temporally-Invariant_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16268 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
SunStage: Portrait Reconstruction and Relighting Using the Sun as a Light Stage | Yifan Wang, Aleksander Holynski, Xiuming Zhang, Xuaner Zhang | A light stage uses a series of calibrated cameras and lights to capture a subject's facial appearance under varying illumination and viewpoint. This captured information is crucial for facial reconstruction and relighting. Unfortunately, light stages are often inaccessible: they are expensive and require significant technical expertise for construction and operation. In this paper, we present SunStage: a lightweight alternative to a light stage that captures comparable data using only a smartphone camera and the sun. Our method only requires the user to capture a selfie video outdoors, rotating in place, and uses the varying angles between the sun and the face as guidance in joint reconstruction of facial geometry, reflectance, camera pose, and lighting parameters. Despite the in-the-wild un-calibrated setting, our approach is able to reconstruct detailed facial appearance and geometry, enabling compelling effects such as relighting, novel view synthesis, and reflectance editing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_SunStage_Portrait_Reconstruction_and_Relighting_Using_the_Sun_as_a_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_SunStage_Portrait_Reconstruction_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.03648 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_SunStage_Portrait_Reconstruction_and_Relighting_Using_the_Sun_as_a_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_SunStage_Portrait_Reconstruction_and_Relighting_Using_the_Sun_as_a_CVPR_2023_paper.html | CVPR 2023 | null |
Private Image Generation With Dual-Purpose Auxiliary Classifier | Chen Chen, Daochang Liu, Siqi Ma, Surya Nepal, Chang Xu | Privacy-preserving image generation has been important for segments such as medical domains that have sensitive and limited data. The benefits of guaranteed privacy come at the costs of generated images' quality and utility due to the privacy budget constraints. The utility is currently measured by the gen2real accuracy (g2r%), i.e., the accuracy on real data of a downstream classifier trained using generated data. However, apart from this standard utility, we identify the "reversed utility" as another crucial aspect, which computes the accuracy on generated data of a classifier trained using real data, dubbed as real2gen accuracy (r2g%). Jointly considering these two views of utility, the standard and the reversed, could help the generation model better improve transferability between fake and real data. Therefore, we propose a novel private image generation method that incorporates a dual-purpose auxiliary classifier, which alternates between learning from real data and fake data, into the training of differentially private GANs. Additionally, our deliberate training strategies such as sequential training contributes to accelerating the generator's convergence and further boosting the performance upon exhausting the privacy budget. Our results achieve new state-of-the-arts over all metrics on three benchmarks: MNIST, Fashion-MNIST, and CelebA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Private_Image_Generation_With_Dual-Purpose_Auxiliary_Classifier_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Private_Image_Generation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Private_Image_Generation_With_Dual-Purpose_Auxiliary_Classifier_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Private_Image_Generation_With_Dual-Purpose_Auxiliary_Classifier_CVPR_2023_paper.html | CVPR 2023 | null |
3D-POP - An Automated Annotation Approach to Facilitate Markerless 2D-3D Tracking of Freely Moving Birds With Marker-Based Motion Capture | Hemal Naik, Alex Hoi Hang Chan, Junran Yang, Mathilde Delacoux, Iain D. Couzin, Fumihiro Kano, Máté Nagy | Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. However, large datasets of annotated images of animals for markerless pose tracking, especially high-resolution images taken from multiple angles with accurate 3D annotations, are still scant. Here, we propose a method that uses a motion capture (mo-cap) system to obtain a large amount of annotated data on animal movement and posture (2D and 3D) in a semi-automatic manner. Our method is novel in that it extracts the 3D positions of morphological keypoints (e.g eyes, beak, tail) in reference to the positions of markers attached to the animals. Using this method, we obtained, and offer here, a new dataset - 3D-POP with approximately 300k annotated frames (4 million instances) in the form of videos having groups of one to ten freely moving birds from 4 different camera views in a 3.6m x 4.2m area. 3D-POP is the first dataset of flocking birds with accurate keypoint annotations in 2D and 3D along with bounding box and individual identities and will facilitate the development of solutions for problems of 2D to 3D markerless pose, trajectory tracking, and identification in birds. | https://openaccess.thecvf.com/content/CVPR2023/papers/Naik_3D-POP_-_An_Automated_Annotation_Approach_to_Facilitate_Markerless_2D-3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Naik_3D-POP_-_An_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Naik_3D-POP_-_An_Automated_Annotation_Approach_to_Facilitate_Markerless_2D-3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Naik_3D-POP_-_An_Automated_Annotation_Approach_to_Facilitate_Markerless_2D-3D_CVPR_2023_paper.html | CVPR 2023 | null |
SOOD: Towards Semi-Supervised Oriented Object Detection | Wei Hua, Dingkang Liang, Jingyu Li, Xiaolong Liu, Zhikang Zou, Xiaoqing Ye, Xiang Bai | Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://github.com/HamPerdredes/SOOD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hua_SOOD_Towards_Semi-Supervised_Oriented_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hua_SOOD_Towards_Semi-Supervised_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04515 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hua_SOOD_Towards_Semi-Supervised_Oriented_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hua_SOOD_Towards_Semi-Supervised_Oriented_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Unified Keypoint-Based Action Recognition Framework via Structured Keypoint Pooling | Ryo Hachiuma, Fumiaki Sato, Taiki Sekii | This paper simultaneously addresses three limitations associated with conventional skeleton-based action recognition; skeleton detection and tracking errors, poor variety of the targeted actions, as well as person-wise and frame-wise action recognition. A point cloud deep-learning paradigm is introduced to the action recognition, and a unified framework along with a novel deep neural network architecture called Structured Keypoint Pooling is proposed. The proposed method sparsely aggregates keypoint features in a cascaded manner based on prior knowledge of the data structure (which is inherent in skeletons), such as the instances and frames to which each keypoint belongs, and achieves robustness against input errors. Its less constrained and tracking-free architecture enables time-series keypoints consisting of human skeletons and nonhuman object contours to be efficiently treated as an input 3D point cloud and extends the variety of the targeted action. Furthermore, we propose a Pooling-Switching Trick inspired by Structured Keypoint Pooling. This trick switches the pooling kernels between the training and inference phases to detect person-wise and frame-wise actions in a weakly supervised manner using only video-level action labels. This trick enables our training scheme to naturally introduce novel data augmentation, which mixes multiple point clouds extracted from different videos. In the experiments, we comprehensively verify the effectiveness of the proposed method against the limitations, and the method outperforms state-of-the-art skeleton-based action recognition and spatio-temporal action localization methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hachiuma_Unified_Keypoint-Based_Action_Recognition_Framework_via_Structured_Keypoint_Pooling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hachiuma_Unified_Keypoint-Based_Action_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15270 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hachiuma_Unified_Keypoint-Based_Action_Recognition_Framework_via_Structured_Keypoint_Pooling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hachiuma_Unified_Keypoint-Based_Action_Recognition_Framework_via_Structured_Keypoint_Pooling_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-View Reconstruction Using Signed Ray Distance Functions (SRDF) | Pierre Zins, Yuanlu Xu, Edmond Boyer, Stefanie Wuhrer, Tony Tung | In this paper, we investigate a new optimization framework for multi-view 3D shape reconstructions. Recent differentiable rendering approaches have provided breakthrough performances with implicit shape representations though they can still lack precision in the estimated geometries. On the other hand multi-view stereo methods can yield pixel wise geometric accuracy with local depth predictions along viewing rays. Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays. The approach retains pixel-accuracy while benefiting from volumetric integration in the optimization. To this aim, depths are optimized by evaluating, at each 3D location within the volumetric discretization, the agreement between the depth prediction consistency and the photometric consistency for the corresponding pixels. The optimization is agnostic to the associated photo-consistency term which can vary from a median-based baseline to more elaborate criteria, learned functions. Our experiments demonstrate the benefit of the volumetric integration with depth predictions. They also show that our approach outperforms existing approaches over standard 3D benchmarks with better geometry estimations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zins_Multi-View_Reconstruction_Using_Signed_Ray_Distance_Functions_SRDF_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zins_Multi-View_Reconstruction_Using_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.00082 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zins_Multi-View_Reconstruction_Using_Signed_Ray_Distance_Functions_SRDF_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zins_Multi-View_Reconstruction_Using_Signed_Ray_Distance_Functions_SRDF_CVPR_2023_paper.html | CVPR 2023 | null |
Beyond mAP: Towards Better Evaluation of Instance Segmentation | Rohit Jena, Lukas Zhornyak, Nehal Doiphode, Pratik Chaudhari, Vivek Buch, James Gee, Jianbo Shi | Correctness of instance segmentation constitutes counting the number of objects, correctly localizing all predictions and classifying each localized prediction. Average Precision is the de-facto metric used to measure all these constituents of segmentation. However, this metric does not penalize duplicate predictions in the high-recall range, and cannot distinguish instances that are localized correctly but categorized incorrectly. This weakness has inadvertently led to network designs that achieve significant gains in AP but also introduce a large number of false positives. We therefore cannot rely on AP to choose a model that provides an optimal tradeoff between false positives and high recall. To resolve this dilemma, we review alternative metrics in the literature and propose two new measures to explicitly measure the amount of both spatial and categorical duplicate predictions. We also propose a Semantic Sorting and NMS module to remove these duplicates based on a pixel occupancy matching scheme. Experiments show that modern segmentation networks have significant gains in AP, but also contain a considerable amount of duplicates. Our Semantic Sorting and NMS can be added as a plug-and-play module to mitigate hedged predictions and preserve AP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jena_Beyond_mAP_Towards_Better_Evaluation_of_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jena_Beyond_mAP_Towards_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.01614 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jena_Beyond_mAP_Towards_Better_Evaluation_of_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jena_Beyond_mAP_Towards_Better_Evaluation_of_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Generating Aligned Pseudo-Supervision From Non-Aligned Data for Image Restoration in Under-Display Camera | Ruicheng Feng, Chongyi Li, Huaijin Chen, Shuai Li, Jinwei Gu, Chen Change Loy | Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps. In this work, we revisit the classic stereo setup for training data collection -- capturing two images of the same scene with one UDC and one standard camera. The key idea is to "copy" details from a high-quality reference image and "paste" them on the UDC image. While being able to generate real training pairs, this setting is susceptible to spatial misalignment due to perspective and depth of field changes. The problem is further compounded by the large domain discrepancy between the UDC and normal images, which is unique to UDC restoration. In this paper, we mitigate the non-trivial domain discrepancy and spatial misalignment through a novel Transformer-based framework that generates well-aligned yet high-quality target data for the corresponding UDC input. This is made possible through two carefully designed components, namely, the Domain Alignment Module (DAM) and Geometric Alignment Module (GAM), which encourage robust and accurate discovery of correspondence between the UDC and normal views. Extensive experiments show that high-quality and well-aligned pseudo UDC training pairs are beneficial for training a robust restoration network. Code and the dataset are available at https://github.com/jnjaby/AlignFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Generating_Aligned_Pseudo-Supervision_From_Non-Aligned_Data_for_Image_Restoration_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Generating_Aligned_Pseudo-Supervision_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06019 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Generating_Aligned_Pseudo-Supervision_From_Non-Aligned_Data_for_Image_Restoration_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Generating_Aligned_Pseudo-Supervision_From_Non-Aligned_Data_for_Image_Restoration_in_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Cross-Modal Retrieval With Set of Diverse Embeddings | Dongwon Kim, Namyup Kim, Suha Kwak | Cross-modal retrieval across image and text modalities is a challenging task due to its inherent ambiguity: An image often exhibits various situations, and a caption can be coupled with diverse images. Set-based embedding has been studied as a solution to this problem. It seeks to encode a sample into a set of different embedding vectors that capture different semantics of the sample. In this paper, we present a novel set-based embedding method, which is distinct from previous work in two aspects. First, we present a new similarity function called smooth-Chamfer similarity, which is designed to alleviate the side effects of existing similarity functions for set-based embedding. Second, we propose a novel set prediction module to produce a set of embedding vectors that effectively captures diverse semantics of input by the slot attention mechanism. Our method is evaluated on the COCO and Flickr30K datasets across different visual backbones, where it outperforms existing methods including ones that demand substantially larger computation at inference. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Improving_Cross-Modal_Retrieval_With_Set_of_Diverse_Embeddings_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Improving_Cross-Modal_Retrieval_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Improving_Cross-Modal_Retrieval_With_Set_of_Diverse_Embeddings_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Improving_Cross-Modal_Retrieval_With_Set_of_Diverse_Embeddings_CVPR_2023_paper.html | CVPR 2023 | null |
BASiS: Batch Aligned Spectral Embedding Space | Or Streicher, Ido Cohen, Guy Gilboa | Graph is a highly generic and diverse representation, suitable for almost any data processing problem. Spectral graph theory has been shown to provide powerful algorithms, backed by solid linear algebra theory. It thus can be extremely instrumental to design deep network building blocks with spectral graph characteristics. For instance, such a network allows the design of optimal graphs for certain tasks or obtaining a canonical orthogonal low-dimensional embedding of the data. Recent attempts to solve this problem were based on minimizing Rayleigh-quotient type losses. We propose a different approach of directly learning the graph's eigensapce. A severe problem of the direct approach, applied in batch-learning, is the inconsistent mapping of features to eigenspace coordinates in different batches. We analyze the degrees of freedom of learning this task using batches and propose a stable alignment mechanism that can work both with batch changes and with graph-metric changes. We show that our learnt spectral embedding is better in terms of NMI, ACC, Grassman distnace, orthogonality and classification accuracy, compared to SOTA. In addition, the learning is more stable. | https://openaccess.thecvf.com/content/CVPR2023/papers/Streicher_BASiS_Batch_Aligned_Spectral_Embedding_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Streicher_BASiS_Batch_Aligned_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16960 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Streicher_BASiS_Batch_Aligned_Spectral_Embedding_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Streicher_BASiS_Batch_Aligned_Spectral_Embedding_Space_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Pixel Composition for 3D-4D View Synthesis From Multi-Views | Aayush Bansal, Michael Zollhöfer | We present Neural Pixel Composition (NPC), a novel approach for continuous 3D-4D view synthesis given only a discrete set of multi-view observations as input. Existing state-of-the-art approaches require dense multi-view supervision and an extensive computational budget. The proposed formulation reliably operates on sparse and wide-baseline multi-view imagery and can be trained efficiently within a few seconds to 10 minutes for hi-res (12MP) content, i.e., 200-400X faster convergence than existing methods. Crucial to our approach are two core novelties: 1) a representation of a pixel that contains color and depth information accumulated from multi-views for a particular location and time along a line of sight, and 2) a multi-layer perceptron (MLP) that enables the composition of this rich information provided for a pixel location to obtain the final color output. We experiment with a large variety of multi-view sequences, compare to existing approaches, and achieve better results in diverse and challenging settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bansal_Neural_Pixel_Composition_for_3D-4D_View_Synthesis_From_Multi-Views_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bansal_Neural_Pixel_Composition_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bansal_Neural_Pixel_Composition_for_3D-4D_View_Synthesis_From_Multi-Views_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bansal_Neural_Pixel_Composition_for_3D-4D_View_Synthesis_From_Multi-Views_CVPR_2023_paper.html | CVPR 2023 | null |
DCFace: Synthetic Face Generation With Dual Condition Diffusion Model | Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu | Generating synthetic datasets for training face recognition models is challenging because dataset generation entails more than creating high fidelity images. It involves generating multiple images of same subjects under different factors (e.g., variations in pose, illumination, expression, aging and occlusion) which follows the real image conditional distribution. Previous works have studied the generation of synthetic datasets using GAN or 3D models. In this work, we approach the problem from the aspect of combining subject appearance (ID) and external factor (style) conditions. These two conditions provide a direct way to control the inter-class and intra-class variations. To this end, we propose a Dual Condition Face Generator (DCFace) based on a diffusion model. Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control. Face recognition models trained on synthetic images from the proposed DCFace provide higher verification accuracies compared to previous works by 6.11% on average in 4 out of 5 test datasets, LFW, CFP-FP, CPLFW, AgeDB and CALFW. Model, code, and synthetic dataset are available at https://github.com/mk-minchul/dcface | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_DCFace_Synthetic_Face_Generation_With_Dual_Condition_Diffusion_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_DCFace_Synthetic_Face_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.07060 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_DCFace_Synthetic_Face_Generation_With_Dual_Condition_Diffusion_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_DCFace_Synthetic_Face_Generation_With_Dual_Condition_Diffusion_Model_CVPR_2023_paper.html | CVPR 2023 | null |
CRAFT: Concept Recursive Activation FacTorization for Explainability | Thomas Fel, Agustin Picard, Louis Béthune, Thibaut Boissin, David Vigouroux, Julien Colin, Rémi Cadène, Thomas Serre | Attribution methods are a popular class of explainability methods that use heatmaps to depict the most important areas of an image that drive a model decision. Nevertheless, recent work has shown that these methods have limited utility in practice, presumably because they only highlight the most salient parts of an image (i.e., "where" the model looked) and do not communicate any information about "what" the model saw at those locations. In this work, we try to fill in this gap with Craft -- a novel approach to identify both "what" and "where" by generating concept-based explanations. We introduce 3 new ingredients to the automatic concept extraction literature: (i) a recursive strategy to detect and decompose concepts across layers, (ii) a novel method for a more faithful estimation of concept importance using Sobol indices, and (iii) the use of implicit differentiation to unlock Concept Attribution Maps. We conduct both human and computer vision experiments to demonstrate the benefits of the proposed approach. We show that our recursive decomposition generates meaningful and accurate concepts and that the proposed concept importance estimation technique is more faithful to the model than previous methods. When evaluating the usefulness of the method for human experimenters on the utility benchmark, we find that our approach significantly improves on two of the three test scenarios (while none of the current methods including ours help on the third). Overall, our study suggests that, while much work remains toward the development of general explainability methods that are useful in practical scenarios, the identification of meaningful concepts at the proper level of granularity yields useful and complementary information beyond that afforded by attribution methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fel_CRAFT_Concept_Recursive_Activation_FacTorization_for_Explainability_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fel_CRAFT_Concept_Recursive_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fel_CRAFT_Concept_Recursive_Activation_FacTorization_for_Explainability_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fel_CRAFT_Concept_Recursive_Activation_FacTorization_for_Explainability_CVPR_2023_paper.html | CVPR 2023 | null |
Policy Adaptation From Foundation Model Feedback | Yuying Ge, Annabella Macaluso, Li Erran Li, Ping Luo, Xiaolong Wang | Recent progress on vision-language foundation models have brought significant advancement to building general-purpose robots. By using the pre-trained models to encode the scene and instructions as inputs for decision making, the instruction-conditioned policy can generalize across different objects and tasks. While this is encouraging, the policy still fails in most cases given an unseen task or environment. In this work, we propose Policy Adaptation from Foundation model Feedback (PAFF). When deploying the trained policy to a new task or a new environment, we first let the policy play with randomly generated instructions to record the demonstrations. While the execution could be wrong, we can use the pre-trained foundation models to provide feedback to relabel the demonstrations. This automatically provides new pairs of demonstration-instruction data for policy fine-tuning. We evaluate our method on a broad range of experiments with the focus on generalization on unseen objects, unseen tasks, unseen environments, and sim-to-real transfer. We show PAFF improves baselines by a large margin in all cases. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ge_Policy_Adaptation_From_Foundation_Model_Feedback_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ge_Policy_Adaptation_From_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.07398 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ge_Policy_Adaptation_From_Foundation_Model_Feedback_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ge_Policy_Adaptation_From_Foundation_Model_Feedback_CVPR_2023_paper.html | CVPR 2023 | null |
Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants With No False Negatives and No False Positives | Daniel Widdowson, Vitaliy Kurlin | Rigid structures such as cars or any other solid objects are often represented by finite clouds of unlabeled points. The most natural equivalence on these point clouds is rigid motion or isometry maintaining all inter-point distances. Rigid patterns of point clouds can be reliably compared only by complete isometry invariants that can also be called equivariant descriptors without false negatives (isometric clouds having different descriptions) and without false positives (non-isometric clouds with the same description). Noise and motion in data motivate a search for invariants that are continuous under perturbations of points in a suitable metric. We propose the first continuous and complete invariant of unlabeled clouds in any Euclidean space. For a fixed dimension, the new metric for this invariant is computable in a polynomial time in the number of points. | https://openaccess.thecvf.com/content/CVPR2023/papers/Widdowson_Recognizing_Rigid_Patterns_of_Unlabeled_Point_Clouds_by_Complete_and_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15385 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Widdowson_Recognizing_Rigid_Patterns_of_Unlabeled_Point_Clouds_by_Complete_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Widdowson_Recognizing_Rigid_Patterns_of_Unlabeled_Point_Clouds_by_Complete_and_CVPR_2023_paper.html | CVPR 2023 | null |
N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution | Haram Choi, Jeongmin Lee, Jihoon Yang | While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://github.com/rami0205/NGramSwin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_N-Gram_in_Swin_Transformers_for_Efficient_Lightweight_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_N-Gram_in_Swin_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_N-Gram_in_Swin_Transformers_for_Efficient_Lightweight_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_N-Gram_in_Swin_Transformers_for_Efficient_Lightweight_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-DETR: Semi-Supervised Object Detection With Detection Transformers | Jiacheng Zhang, Xiangru Lin, Wei Zhang, Kuo Wang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li | We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo ground-truth bounding box is inaccurate, leading to training inefficiency; (2) DETR-based detectors lack deterministic correspondence between the input query and its prediction output, which hinders the applicability of the consistency-based regularization widely used in current SSOD methods. We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector, to tackle these problems. Specifically, we propose a Stage-wise Hybrid Matching strategy that com- bines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage. Besides, we introduce a Cross-view Query Consistency method to learn the semantic feature invariance of object queries from different views while avoiding the need to find deterministic query correspondence. Furthermore, we propose a Cost-based Pseudo Label Mining module to dynamically mine more pseudo boxes based on the matching cost of pseudo ground truth bounding boxes for consistency training. Extensive experiments on all SSOD settings of both COCO and Pascal VOC benchmark datasets show that our Semi-DETR method outperforms all state-of-the-art methods by clear margins. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Semi-DETR_Semi-Supervised_Object_Detection_With_Detection_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Semi-DETR_Semi-Supervised_Object_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Semi-DETR_Semi-Supervised_Object_Detection_With_Detection_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Semi-DETR_Semi-Supervised_Object_Detection_With_Detection_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Infinite Photorealistic Worlds Using Procedural Generation | Alexander Raistrick, Lahav Lipson, Zeyu Ma, Lingjie Mei, Mingzhe Wang, Yiming Zuo, Karhan Kayan, Hongyu Wen, Beining Han, Yihan Wang, Alejandro Newell, Hei Law, Ankit Goyal, Kaiyu Yang, Jia Deng | We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction. We expect Infinigen to be a useful resource for computer vision research and beyond. Please visit https://infinigen.org for videos, code and pre-generated data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Raistrick_Infinite_Photorealistic_Worlds_Using_Procedural_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Raistrick_Infinite_Photorealistic_Worlds_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Raistrick_Infinite_Photorealistic_Worlds_Using_Procedural_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Raistrick_Infinite_Photorealistic_Worlds_Using_Procedural_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Diversity-Measurable Anomaly Detection | Wenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen | Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better solve the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Hybrid Neural Rendering for Large-Scale Scenes With Motion Blur | Peng Dai, Yinda Zhang, Xin Yu, Xiaoyang Lyu, Xiaojuan Qi | Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/Hybrid-Rendering-ProjectPage. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dai_Hybrid_Neural_Rendering_for_Large-Scale_Scenes_With_Motion_Blur_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dai_Hybrid_Neural_Rendering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_Hybrid_Neural_Rendering_for_Large-Scale_Scenes_With_Motion_Blur_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_Hybrid_Neural_Rendering_for_Large-Scale_Scenes_With_Motion_Blur_CVPR_2023_paper.html | CVPR 2023 | null |
Perception-Oriented Single Image Super-Resolution Using Optimal Objective Estimation | Seung Ho Park, Young Su Moon, Nam Ik Cho | Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, often generating undesirable artifacts or unnatural details. For this reason, combinations of various losses, such as perceptual, adversarial, and distortion losses, have been attempted, yet it remains challenging to find optimal combinations. Hence, in this paper, we propose a new SISR framework that applies optimal objectives for each region to generate plausible results in overall areas of high-resolution outputs. Specifically, the framework comprises two models: a predictive model that infers an optimal objective map for a given low-resolution (LR) input and a generative model that applies a target objective map to produce the corresponding SR output. The generative model is trained over our proposed objective trajectory representing a set of essential objectives, which enables the single network to learn various SR results corresponding to combined losses on the trajectory. The predictive model is trained using pairs of LR images and corresponding optimal objective maps searched from the objective trajectory. Experimental results on five benchmarks show that the proposed method outperforms state-of-the-art perception-driven SR methods in LPIPS, DISTS, PSNR, and SSIM metrics. The visual results also demonstrate the superiority of our method in perception-oriented reconstruction. The code is available at https://github.com/seungho-snu/SROOE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Perception-Oriented_Single_Image_Super-Resolution_Using_Optimal_Objective_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Perception-Oriented_Single_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13676 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Perception-Oriented_Single_Image_Super-Resolution_Using_Optimal_Objective_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Perception-Oriented_Single_Image_Super-Resolution_Using_Optimal_Objective_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
GP-VTON: Towards General Purpose Virtual Try-On via Collaborative Local-Flow Global-Parsing Learning | Zhenyu Xie, Zaiyu Huang, Xin Dong, Fuwei Zhao, Haoye Dong, Xijin Zhang, Feida Zhu, Xiaodan Liang | Image-based Virtual Try-ON aims to transfer an in-shop garment onto a specific person. Existing methods employ a global warping module to model the anisotropic deformation for different garment parts, which fails to preserve the semantic information of different parts when receiving challenging inputs (e.g, intricate human poses, difficult garments). Moreover, most of them directly warp the input garment to align with the boundary of the preserved region, which usually requires texture squeezing to meet the boundary shape constraint and thus leads to texture distortion. The above inferior performance hinders existing methods from real-world applications. To address these problems and take a step towards real-world virtual try-on, we propose a General-Purpose Virtual Try-ON framework, named GP-VTON, by developing an innovative Local-Flow Global-Parsing (LFGP) warping module and a Dynamic Gradient Truncation (DGT) training strategy. Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs.On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem. Furthermore, our GP-VTON can be easily extended to multi-category scenario and jointly trained by using data from different garment categories. Extensive experiments on two high-resolution benchmarks demonstrate our superiority over the existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_GP-VTON_Towards_General_Purpose_Virtual_Try-On_via_Collaborative_Local-Flow_Global-Parsing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_GP-VTON_Towards_General_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_GP-VTON_Towards_General_Purpose_Virtual_Try-On_via_Collaborative_Local-Flow_Global-Parsing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_GP-VTON_Towards_General_Purpose_Virtual_Try-On_via_Collaborative_Local-Flow_Global-Parsing_CVPR_2023_paper.html | CVPR 2023 | null |
A Large-Scale Robustness Analysis of Video Action Recognition Models | Madeline Chantry Schiappa, Naman Biyani, Prudvi Kamtam, Shruti Vyas, Hamid Palangi, Vibhav Vineet, Yogesh S. Rawat | We have seen great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance on existing benchmarks. In this work, we perform a large-scale robustness analysis of these existing models for video action recognition. We focus on robustness against real-world distribution shift perturbations instead of adversarial perturbations. We propose four different benchmark datasets, HMDB51-P, UCF101-P, Kinetics400-P, and SSv2-P to perform this analysis. We study robustness of six state-of-the-art action recognition models against 90 different perturbations. The study reveals some interesting findings, 1) Transformer based models are consistently more robust compared to CNN based models, 2) Pre-training improves robustness for Transformer based models more than CNN based models, and 3) All of the studied models are robust to temporal perturbations for all datasets but SSv2; suggesting the importance of temporal information for action recognition varies based on the dataset and activities. Next, we study the role of augmentations in model robustness and present a real-world dataset, UCF101-DS, which contains realistic distribution shifts, to further validate some of these findings. We believe this study will serve as a benchmark for future research in robust video action recognition. | https://openaccess.thecvf.com/content/CVPR2023/papers/Schiappa_A_Large-Scale_Robustness_Analysis_of_Video_Action_Recognition_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Schiappa_A_Large-Scale_Robustness_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Schiappa_A_Large-Scale_Robustness_Analysis_of_Video_Action_Recognition_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Schiappa_A_Large-Scale_Robustness_Analysis_of_Video_Action_Recognition_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning | Xiaocheng Lu, Song Guo, Ziming Liu, Jingcai Guo | Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts formed by known states and objects during training. Existing methods either learn the combined state-object representation, challenging the generalization of unseen compositions, or design two classifiers to identify state and object separately from image features, ignoring the intrinsic relationship between them. To jointly eliminate the above issues and construct a more robust CZSL system, we propose a novel framework termed Decomposed Fusion with Soft Prompt (DFSP), by involving vision-language models (VLMs) for unseen composition recognition. Specifically, DFSP constructs a vector combination of learnable soft prompts with state and object to establish the joint representation of them. In addition, a cross-modal decomposed fusion module is designed between the language and image branches, which decomposes state and object among language features instead of image features. Notably, being fused with the decomposed features, the image features can be more expressive for learning the relationship with states and objects, respectively, to improve the response of unseen compositions in the pair space, hence narrowing the domain gap between seen and unseen sets. Experimental results on three challenging benchmarks demonstrate that our approach significantly outperforms other state-of-the-art methods by large margins. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Decomposed_Soft_Prompt_Guided_Fusion_Enhancing_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_Decomposed_Soft_Prompt_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10681 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Decomposed_Soft_Prompt_Guided_Fusion_Enhancing_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Decomposed_Soft_Prompt_Guided_Fusion_Enhancing_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Semantic Contrast for Scene-Aware Video Anomaly Detection | Shengyang Sun, Xiaojin Gong | Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware VAD model from normal videos. We first incorporate foreground object and background scene features with high-level semantics by taking advantage of pre-trained video parsing models. Then, building upon the autoencoder-based reconstruction framework, we introduce both scene-level and object-level contrastive learning to enforce the encoded latent features to be compact within the same semantic classes while being separable across different classes. This hierarchical semantic contrast strategy helps to deal with the diversity of normal patterns and also increases their discrimination ability. Moreover, for the sake of tackling rare normal activities, we design a skeleton-based motion augmentation to increase samples and refine the model further. Extensive experiments on three public datasets and scene-dependent mixture datasets validate the effectiveness of our proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Hierarchical_Semantic_Contrast_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Hierarchical_Semantic_Contrast_for_Scene-Aware_Video_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
All-in-Focus Imaging From Event Focal Stack | Hanyue Lou, Minggui Teng, Yixin Yang, Boxin Shi | Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lou_All-in-Focus_Imaging_From_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.html | CVPR 2023 | null |
Video Probabilistic Diffusion Models in Projected Latent Space | Sihyun Yu, Kihyuk Sohn, Subin Kim, Jinwoo Shin | Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial variations. Recent works on diffusion models have shown their potential to solve this challenge, yet they suffer from severe computation- and memory-inefficiency that limit the scalability. To handle this issue, we propose a novel generative model for videos, coined projected latent video diffusion models (PVDM), a probabilistic diffusion model which learns a video distribution in a low-dimensional latent space and thus can be efficiently trained with high-resolution videos under limited resources. Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model. Experiments on popular video generation datasets demonstrate the superiority of PVDM compared with previous video synthesis methods; e.g., PVDM obtains the FVD score of 639.7 on the UCF-101 long video (128 frames) generation benchmark, which improves 1773.4 of the prior state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Video_Probabilistic_Diffusion_Models_in_Projected_Latent_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Video_Probabilistic_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.07685 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Video_Probabilistic_Diffusion_Models_in_Projected_Latent_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Video_Probabilistic_Diffusion_Models_in_Projected_Latent_Space_CVPR_2023_paper.html | CVPR 2023 | null |
Learning 3D Scene Priors With 2D Supervision | Yinyu Nie, Angela Dai, Xiaoguang Han, Matthias Nießner | Holistic 3D scene understanding entails estimation of both layout configuration and object geometry in a 3D environment. Recent works have shown advances in 3D scene estimation from various input modalities (e.g., images, 3D scans), by leveraging 3D supervision (e.g., 3D bounding boxes or CAD models), for which collection at scale is expensive and often intractable. To address this shortcoming, we propose a new method to learn 3D scene priors of layout and shape without requiring any 3D ground truth. Instead, we rely on 2D supervision from multi-view RGB images. Our method represents a 3D scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories, 3D bounding boxes, and meshes. With our trained autoregressive decoder representing the scene prior, our method facilitates many downstream applications, including scene synthesis, interpolation, and single-view reconstruction. Experiments on 3D-FRONT and ScanNet show that our method outperforms state of the art in single-view reconstruction, and achieves state-of-the-art results in scene synthesis against baselines which require for 3D supervision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nie_Learning_3D_Scene_Priors_With_2D_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nie_Learning_3D_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14157 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nie_Learning_3D_Scene_Priors_With_2D_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nie_Learning_3D_Scene_Priors_With_2D_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Blind Video Deflickering by Neural Filtering With a Flawed Atlas | Chenyang Lei, Xuanchi Ren, Zhaoxiang Zhang, Qifeng Chen | Many videos contain flickering artifacts; common causes of flicker include video processing algorithms, video generation algorithms, and capturing videos under specific situations. Prior work usually requires specific guidance such as the flickering frequency, manual annotations, or extra consistent videos to remove the flicker. In this work, we propose a general flicker removal framework that only receives a single flickering video as input without additional guidance. Since it is blind to a specific flickering type or guidance, we name this "blind deflickering." The core of our approach is utilizing the neural atlas in cooperation with a neural filtering strategy. The neural atlas is a unified representation for all frames in a video that provides temporal consistency guidance but is flawed in many cases. To this end, a neural network is trained to mimic a filter to learn the consistent features (e.g., color, brightness) and avoid introducing the artifacts in the atlas. To validate our method, we construct a dataset that contains diverse real-world flickering videos. Extensive experiments show that our method achieves satisfying deflickering performance and even outperforms baselines that use extra guidance on a public benchmark. The source code is publicly available at https://chenyanglei.github.io/deflicker. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lei_Blind_Video_Deflickering_by_Neural_Filtering_With_a_Flawed_Atlas_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08120 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_Blind_Video_Deflickering_by_Neural_Filtering_With_a_Flawed_Atlas_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_Blind_Video_Deflickering_by_Neural_Filtering_With_a_Flawed_Atlas_CVPR_2023_paper.html | CVPR 2023 | null |
Label-Free Liver Tumor Segmentation | Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, Alan L. Yuille, Zongwei Zhou | We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors--this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Label-Free_Liver_Tumor_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Label-Free_Liver_Tumor_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14869 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Label-Free_Liver_Tumor_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Label-Free_Liver_Tumor_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Grid-Guided Neural Radiance Fields for Large Urban Scenes | Linning Xu, Yuanbo Xiangli, Sida Peng, Xingang Pan, Nanxuan Zhao, Christian Theobalt, Bo Dai, Dahua Lin | Purely MLP-based neural radiance fields (NeRF-based methods) often suffer from underfitting with blurred renderings on large-scale scenes due to limited model capacity. Recent approaches propose to geographically divide the scene and adopt multiple sub-NeRFs to model each region individually, leading to linear scale-up in training costs and the number of sub-NeRFs as the scene expands. An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene with increased grid resolutions. However, the feature grid tends to be less constrained and often reaches suboptimal solutions, producing noisy artifacts in renderings, especially in regions with complex geometry and texture. In this work, we present a new framework that realizes high-fidelity rendering on large urban scenes while being computationally efficient. We propose to use a compact multi-resolution ground feature plane representation to coarsely capture the scene, and complement it with positional encoding inputs through another NeRF branch for rendering in a joint learning fashion. We show that such an integration can utilize the advantages of two alternative solutions: a light-weighted NeRF is sufficient, under the guidance of the feature grid representation, to render photorealistic novel views with fine details; and the jointly optimized ground feature planes, can meanwhile gain further refinements, forming a more accurate and compact feature space and output much more natural rendering results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Grid-Guided_Neural_Radiance_Fields_for_Large_Urban_Scenes_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.14001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Grid-Guided_Neural_Radiance_Fields_for_Large_Urban_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Grid-Guided_Neural_Radiance_Fields_for_Large_Urban_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
Defining and Quantifying the Emergence of Sparse Concepts in DNNs | Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang | This paper aims to illustrate the concept-emerging phenomenon in a trained DNN. Specifically, we find that the inference score of a DNN can be disentangled into the effects of a few interactive concepts. These concepts can be understood as inference patterns in a sparse, symbolic graphical model, which explains the DNN. The faithfulness of using such a graphical model to explain the DNN is theoretically guaranteed, because we prove that the graphical model can well mimic the DNN's outputs on an exponential number of different masked samples. Besides, such a graphical model can be further simplified and re-written as an And-Or graph (AOG), without losing much explanation accuracy. The code is released at https://github.com/sjtu-xai-lab/aog. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_Defining_and_Quantifying_the_Emergence_of_Sparse_Concepts_in_DNNs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ren_Defining_and_Quantifying_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2111.06206 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Defining_and_Quantifying_the_Emergence_of_Sparse_Concepts_in_DNNs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Defining_and_Quantifying_the_Emergence_of_Sparse_Concepts_in_DNNs_CVPR_2023_paper.html | CVPR 2023 | null |
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias | Noa Garcia, Yusuke Hirota, Yankun Wu, Yuta Nakashima | The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations. It is known that even small but manually annotated datasets, such as MSCOCO, are affected by societal bias. This problem, far from being solved, may be getting worse with data crawled from the Internet without much control. In addition, the lack of tools to analyze societal bias in big collections of images makes addressing the problem extremely challenging. Our first contribution is to annotate part of the Google Conceptual Captions dataset, widely used for training vision-and-language models, with four demographic and two contextual attributes. Our second contribution is to conduct a comprehensive analysis of the annotations, focusing on how different demographic groups are represented. Our last contribution lies in evaluating three prevailing vision-and-language tasks: image captioning, text-image CLIP embeddings, and text-to-image generation, showing that societal bias is a persistent problem in all of them. | https://openaccess.thecvf.com/content/CVPR2023/papers/Garcia_Uncurated_Image-Text_Datasets_Shedding_Light_on_Demographic_Bias_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Garcia_Uncurated_Image-Text_Datasets_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02828 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Garcia_Uncurated_Image-Text_Datasets_Shedding_Light_on_Demographic_Bias_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Garcia_Uncurated_Image-Text_Datasets_Shedding_Light_on_Demographic_Bias_CVPR_2023_paper.html | CVPR 2023 | null |
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation | Jie Qin, Jie Wu, Pengxiang Yan, Ming Li, Ren Yuxi, Xuefeng Xiao, Yitong Wang, Rui Wang, Shilei Wen, Xin Pan, Xingang Wang | Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios. However, existing methods devote to designing specialized architectures or parameters for specific segmentation tasks. These customized design paradigms lead to fragmentation between various segmentation tasks, thus hindering the uniformity of segmentation models. Hence in this paper, we propose FreeSeg, a generic framework to accomplish Unified, Universal and Open-Vocabulary Image Segmentation. FreeSeg optimizes an all-in-one network via one-shot training and employs the same architecture and parameters to handle diverse segmentation tasks seamlessly in the inference procedure. Additionally, adaptive prompt learning facilitates the unified model to capture task-aware and category-sensitive concepts, improving model robustness in multi-task and varied scenarios. Extensive experimental results demonstrate that FreeSeg establishes new state-of-the-art results in performance and generalization on three segmentation tasks, which outperforms the best task-specific architectures by a large margin: 5.5% mIoU on semantic segmentation, 17.6% mAP on instance segmentation, 20.1% PQ on panoptic segmentation for the unseen class on COCO. Project page: https://FreeSeg.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_FreeSeg_Unified_Universal_and_Open-Vocabulary_Image_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.17225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_FreeSeg_Unified_Universal_and_Open-Vocabulary_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_FreeSeg_Unified_Universal_and_Open-Vocabulary_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
AVFormer: Injecting Vision Into Frozen Speech Models for Zero-Shot AV-ASR | Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid | Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audioonly models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively; and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition. | https://openaccess.thecvf.com/content/CVPR2023/papers/Seo_AVFormer_Injecting_Vision_Into_Frozen_Speech_Models_for_Zero-Shot_AV-ASR_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Seo_AVFormer_Injecting_Vision_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16501 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Seo_AVFormer_Injecting_Vision_Into_Frozen_Speech_Models_for_Zero-Shot_AV-ASR_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Seo_AVFormer_Injecting_Vision_Into_Frozen_Speech_Models_for_Zero-Shot_AV-ASR_CVPR_2023_paper.html | CVPR 2023 | null |
FreeNeRF: Improving Few-Shot Neural Rendering With Free Frequency Regularization | Jiawei Yang, Marco Pavone, Yue Wang | Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields (NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as pre-trained models and extra depth signals, or by using non-trivial patch-based rendering. In this paper, we present Frequency regularized NeRF (FreeNeRF), a surprisingly simple baseline that outperforms previous methods with minimal modifications to plain NeRF. We analyze the key challenges in few-shot neural rendering and find that frequency plays an important role in NeRF's training. Based on this analysis, we propose two regularization terms: one to regularize the frequency range of NeRF's inputs, and the other to penalize the near-camera density fields. Both techniques are "free lunches" that come at no additional computational cost. We demonstrate that even with just one line of code change, the original NeRF can achieve similar performance to other complicated methods in the few-shot setting. FreeNeRF achieves state-of-the-art performance across diverse datasets, including Blender, DTU, and LLFF. We hope that this simple baseline will motivate a rethinking of the fundamental role of frequency in NeRF's training, under both the low-data regime and beyond. This project is released at https://jiawei-yang.github.io/FreeNeRF/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_FreeNeRF_Improving_Few-Shot_Neural_Rendering_With_Free_Frequency_Regularization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_FreeNeRF_Improving_Few-Shot_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07418 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_FreeNeRF_Improving_Few-Shot_Neural_Rendering_With_Free_Frequency_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_FreeNeRF_Improving_Few-Shot_Neural_Rendering_With_Free_Frequency_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
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