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DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment | Heyuan Li, Bo Wang, Yu Cheng, Mohan Kankanhalli, Robby T. Tan | Sensitivity to severe occlusion and large view angles limits the usage scenarios of the existing monocular 3D dense face alignment methods. The state-of-the-art 3DMM-based method, directly regresses the model's coefficients, underutilizing the low-level 2D spatial and semantic information, which can actually offer cues for face shape and orientation. In this work, we demonstrate how modeling 3D facial geometry in image and model space jointly can solve the occlusion and view angle problems. Instead of predicting the whole face directly, we regress image space features in the visible facial region by dense prediction first. Subsequently, we predict our model's coefficients based on the regressed feature of the visible regions, leveraging the prior knowledge of whole face geometry from the morphable models to complete the invisible regions. We further propose a fusion network that combines the advantages of both the image and model space predictions to achieve high robustness and accuracy in unconstrained scenarios. Thanks to the proposed fusion module, our method is robust not only to occlusion and large pitch and roll view angles, which is the benefit of our image space approach, but also to noise and large yaw angles, which is the benefit of our model space method. Comprehensive evaluations demonstrate the superior performance of our method compared with the state-of-the-art methods. On the 3D dense face alignment task, we achieve 3.80% NME on the AFLW2000-3D dataset, which outperforms the state-of-the-art method by 5.5%. Code is available at https://github.com/lhyfst/DSFNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_DSFNet_Dual_Space_Fusion_Network_for_Occlusion-Robust_3D_Dense_Face_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_DSFNet_Dual_Space_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DSFNet_Dual_Space_Fusion_Network_for_Occlusion-Robust_3D_Dense_Face_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DSFNet_Dual_Space_Fusion_Network_for_Occlusion-Robust_3D_Dense_Face_CVPR_2023_paper.html | CVPR 2023 | null |
MoStGAN-V: Video Generation With Temporal Motion Styles | Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny | Video generation remains a challenging task due to spatiotemporal complexity and the requirement of synthesizing diverse motions with temporal consistency. Previous works attempt to generate videos in arbitrary lengths either in an autoregressive manner or regarding time as a continuous signal. However, they struggle to synthesize detailed and diverse motions with temporal coherence and tend to generate repetitive scenes after a few time steps. In this work, we argue that a single time-agnostic latent vector of style-based generator is insufficient to model various and temporally-consistent motions. Hence, we introduce additional time-dependent motion styles to model diverse motion patterns. In addition, a Motion Style Attention modulation mechanism, dubbed as MoStAtt, is proposed to augment frames with vivid dynamics for each specific scale (i.e., layer), which assigns attention score for each motion style w.r.t deconvolution filter weights in the target synthesis layer and softly attends different motion styles for weight modulation. Experimental results show our model achieves state-of-the-art performance on four unconditional 256^2 video synthesis benchmarks trained with only 3 frames per clip and produces better qualitative results with respect to dynamic motions. Code and videos have been made available at https://github.com/xiaoqian-shen/MoStGAN-V. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_MoStGAN-V_Video_Generation_With_Temporal_Motion_Styles_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_MoStGAN-V_Video_Generation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_MoStGAN-V_Video_Generation_With_Temporal_Motion_Styles_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_MoStGAN-V_Video_Generation_With_Temporal_Motion_Styles_CVPR_2023_paper.html | CVPR 2023 | null |
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once | Tao Xie, Shiguang Wang, Ke Wang, Linqi Yang, Zhiqiang Jiang, Xingcheng Zhang, Kun Dai, Ruifeng Li, Jian Cheng | In this work, we show that it is feasible to perform multiple tasks concurrently on point cloud with a straightforward yet effective multi-task network. Our framework, Poly-PC, tackles the inherent obstacles (e.g., different model architectures caused by task bias and conflicting gradients caused by multiple dataset domains, etc.) of multi-task learning on point cloud. Specifically, we propose a residual set abstraction (Res-SA) layer for efficient and effective scaling in both width and depth of the network, hence accommodating the needs of various tasks. We develop a weight-entanglement-based one-shot NAS technique to find optimal architectures for all tasks. Moreover, such technique entangles the weights of multiple tasks in each layer to offer task-shared parameters for efficient storage deployment while providing ancillary task-specific parameters for learning task-related features. Finally, to facilitate the training of Poly-PC, we introduce a task-prioritization-based gradient balance algorithm that leverages task prioritization to reconcile conflicting gradients, ensuring high performance for all tasks. Benefiting from the suggested techniques, models optimized by Poly-PC collectively for all tasks keep fewer total FLOPs and parameters and outperform previous methods. We also demonstrate that Poly-PC allows incremental learning and evades catastrophic forgetting when tuned to a new task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Poly-PC_A_Polyhedral_Network_for_Multiple_Point_Cloud_Tasks_at_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Poly-PC_A_Polyhedral_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Poly-PC_A_Polyhedral_Network_for_Multiple_Point_Cloud_Tasks_at_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Poly-PC_A_Polyhedral_Network_for_Multiple_Point_Cloud_Tasks_at_CVPR_2023_paper.html | CVPR 2023 | null |
HandsOff: Labeled Dataset Generation With No Additional Human Annotations | Austin Xu, Mariya I. Vasileva, Achal Dave, Arjun Seshadri | Recent work leverages the expressive power of genera- tive adversarial networks (GANs) to generate labeled syn- thetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of generated labels. We in- troduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and cor- responding labels after being trained on less than 50 pre- existing labeled images. Our framework avoids the practi- cal drawbacks of prior work by unifying the field of GAN in- version with dataset generation. We generate datasets with rich pixel-wise labels in multiple challenging domains such as faces, cars, full-body human poses, and urban driving scenes. Our method achieves state-of-the-art performance in semantic segmentation, keypoint detection, and depth es- timation compared to prior dataset generation approaches and transfer learning baselines. We additionally showcase its ability to address broad challenges in model develop- ment which stem from fixed, hand-annotated datasets, such as the long-tail problem in semantic segmentation. Project page: austinxu87.github.io/handsoff. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_HandsOff_Labeled_Dataset_Generation_With_No_Additional_Human_Annotations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_HandsOff_Labeled_Dataset_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.12645 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_HandsOff_Labeled_Dataset_Generation_With_No_Additional_Human_Annotations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_HandsOff_Labeled_Dataset_Generation_With_No_Additional_Human_Annotations_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module | Linzhi Huang, Yulong Li, Hongbo Tian, Yue Yang, Xiangang Li, Weihong Deng, Jieping Ye | In this paper, we delve into semi-supervised 2D human pose estimation. The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. (ii) The negative impact of noise pseudo labels on training. Moreover, the labels used for 2D human pose estimation are relatively complex: keypoint category and keypoint position. To solve the problems mentioned above, we propose a semi-supervised 2D human pose estimation framework driven by a position inconsistency pseudo label correction module (SSPCM). We introduce an additional auxiliary teacher and use the pseudo labels generated by the two teacher model in different periods to calculate the inconsistency score and remove outliers. Then, the two teacher models are updated through interactive training, and the student model is updated using the pseudo labels generated by two teachers. To further improve the performance of the student model, we use the semi-supervised Cut-Occlude based on pseudo keypoint perception to generate more hard and effective samples. In addition, we also proposed a new indoor overhead fisheye human keypoint dataset WEPDTOF-Pose. Extensive experiments demonstrate that our method outperforms the previous best semi-supervised 2D human pose estimation method. We will release the code and dataset at https://github.com/hlz0606/SSPCM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Semi-Supervised_2D_Human_Pose_Estimation_Driven_by_Position_Inconsistency_Pseudo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Semi-Supervised_2D_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.04346 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Semi-Supervised_2D_Human_Pose_Estimation_Driven_by_Position_Inconsistency_Pseudo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Semi-Supervised_2D_Human_Pose_Estimation_Driven_by_Position_Inconsistency_Pseudo_CVPR_2023_paper.html | CVPR 2023 | null |
ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data | Haojie Zhao, Junsong Chen, Lijun Wang, Huchuan Lu | Compared with traditional RGB-only visual tracking, few datasets have been constructed for RGB-D tracking. In this paper, we propose ARKitTrack, a new RGB-D tracking dataset for both static and dynamic scenes captured by consumer-grade LiDAR scanners equipped on Apple's iPhone and iPad. ARKitTrack contains 300 RGB-D sequences, 455 targets, and 229.7K video frames in total. Along with the bounding box annotations and frame-level attributes, we also annotate this dataset with 123.9K pixel-level target masks. Besides, the camera intrinsic and camera pose of each frame are provided for future developments. To demonstrate the potential usefulness of this dataset, we further present a unified baseline for both box-level and pixel-level tracking, which integrates RGB features with bird's-eye-view representations to better explore cross-modality 3D geometry. In-depth empirical analysis has verified that the ARKitTrack dataset can significantly facilitate RGB-D tracking and that the proposed baseline method compares favorably against the state of the arts. The source code and dataset will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_ARKitTrack_A_New_Diverse_Dataset_for_Tracking_Using_Mobile_RGB-D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_ARKitTrack_A_New_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_ARKitTrack_A_New_Diverse_Dataset_for_Tracking_Using_Mobile_RGB-D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_ARKitTrack_A_New_Diverse_Dataset_for_Tracking_Using_Mobile_RGB-D_CVPR_2023_paper.html | CVPR 2023 | null |
Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks | Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, Furu Wei | A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEiT-3, which achieves excellent transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We use Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner. Experimental results show that BEiT-3 obtains remarkable performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO). | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Image_as_a_Foreign_Language_BEiT_Pretraining_for_Vision_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Image_as_a_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Image_as_a_Foreign_Language_BEiT_Pretraining_for_Vision_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Image_as_a_Foreign_Language_BEiT_Pretraining_for_Vision_and_CVPR_2023_paper.html | CVPR 2023 | null |
Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection | Qianjiang Hu, Daizong Liu, Wei Hu | 3D object detection from point clouds is crucial in safety-critical autonomous driving. Although many works have made great efforts and achieved significant progress on this task, most of them suffer from expensive annotation cost and poor transferability to unknown data due to the domain gap. Recently, few works attempt to tackle the domain gap in objects, but still fail to adapt to the gap of varying beam-densities between two domains, which is critical to mitigate the characteristic differences of the LiDAR collectors. To this end, we make the attempt to propose a density-insensitive domain adaption framework to address the density-induced domain gap. In particular, we first introduce Random Beam Re-Sampling (RBRS) to enhance the robustness of 3D detectors trained on the source domain to the varying beam-density. Then, we take this pre-trained detector as the backbone model, and feed the unlabeled target domain data into our newly designed task-specific teacher-student framework for predicting its high-quality pseudo labels. To further adapt the property of density-insensitive into the target domain, we feed the teacher and student branches with the same sample of different densities, and propose an Object Graph Alignment (OGA) module to construct two object-graphs between the two branches for enforcing the consistency in both the attribute and relation of cross-density objects. Experimental results on three widely adopted 3D object detection datasets demonstrate that our proposed domain adaption method outperforms the state-of-the-art methods, especially over varying-density data. Code is available at https://github.com/WoodwindHu/DTS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Density-Insensitive_Unsupervised_Domain_Adaption_on_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Density-Insensitive_Unsupervised_Domain_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.09446 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Density-Insensitive_Unsupervised_Domain_Adaption_on_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Density-Insensitive_Unsupervised_Domain_Adaption_on_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Verification of Neural Networks Against LVM-Based Specifications | Harleen Hanspal, Alessio Lomuscio | The deployment of perception systems based on neural networks in safety critical applications requires assurance on their robustness. Deterministic guarantees on network robustness require formal verification. Standard approaches for verifying robustness analyse invariance to analytically defined transformations, but not the diverse and ubiquitous changes involving object pose, scene viewpoint, occlusions, etc. To this end, we present an efficient approach for verifying specifications definable using Latent Variable Models that capture such diverse changes. The approach involves adding an invertible encoding head to the network to be verified, enabling the verification of latent space sets with minimal reconstruction overhead. We report verification experiments for three classes of proposed latent space specifications, each capturing different types of realistic input variations. Differently from previous work in this area, the proposed approach is relatively independent of input dimensionality and scales to a broad class of deep networks and real-world datasets by mitigating the inefficiency and decoder expressivity dependence in the present state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hanspal_Efficient_Verification_of_Neural_Networks_Against_LVM-Based_Specifications_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hanspal_Efficient_Verification_of_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hanspal_Efficient_Verification_of_Neural_Networks_Against_LVM-Based_Specifications_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hanspal_Efficient_Verification_of_Neural_Networks_Against_LVM-Based_Specifications_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Action Changes by Measuring Verb-Adverb Textual Relationships | Davide Moltisanti, Frank Keller, Hakan Bilen, Laura Sevilla-Lara | The goal of this work is to understand the way actions are performed in videos. That is, given a video, we aim to predict an adverb indicating a modification applied to the action (e.g. cut "finely"). We cast this problem as a regression task. We measure textual relationships between verbs and adverbs to generate a regression target representing the action change we aim to learn. We test our approach on a range of datasets and achieve state-of-the-art results on both adverb prediction and antonym classification. Furthermore, we outperform previous work when we lift two commonly assumed conditions: the availability of action labels during testing and the pairing of adverbs as antonyms. Existing datasets for adverb recognition are either noisy, which makes learning difficult, or contain actions whose appearance is not influenced by adverbs, which makes evaluation less reliable. To address this, we collect a new high quality dataset: Adverbs in Recipes (AIR). We focus on instructional recipes videos, curating a set of actions that exhibit meaningful visual changes when performed differently. Videos in AIR are more tightly trimmed and were manually reviewed by multiple annotators to ensure high labelling quality. Results show that models learn better from AIR given its cleaner videos. At the same time, adverb prediction on AIR is challenging, demonstrating that there is considerable room for improvement. | https://openaccess.thecvf.com/content/CVPR2023/papers/Moltisanti_Learning_Action_Changes_by_Measuring_Verb-Adverb_Textual_Relationships_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Moltisanti_Learning_Action_Changes_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.15086 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Moltisanti_Learning_Action_Changes_by_Measuring_Verb-Adverb_Textual_Relationships_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Moltisanti_Learning_Action_Changes_by_Measuring_Verb-Adverb_Textual_Relationships_CVPR_2023_paper.html | CVPR 2023 | null |
Feature Aggregated Queries for Transformer-Based Video Object Detectors | Yiming Cui | Video object detection needs to solve feature degradation situations that rarely happen in the image domain. One solution is to use the temporal information and fuse the features from the neighboring frames. With Transformer-based object detectors getting a better performance on the image domain tasks, recent works began to extend those methods to video object detection. However, those existing Transformer-based video object detectors still follow the same pipeline as those used for classical object detectors, like enhancing the object feature representations by aggregation. In this work, we take a different perspective on video object detection. In detail, we improve the qualities of queries for the Transformer-based models by aggregation. To achieve this goal, we first propose a vanilla query aggregation module that weighted averages the queries according to the features of the neighboring frames. Then, we extend the vanilla module to a more practical version, which generates and aggregates queries according to the features of the input frames. Extensive experimental results validate the effectiveness of our proposed methods: On the challenging ImageNet VID benchmark, when integrated with our proposed modules, the current state-of-the-art Transformer-based object detectors can be improved by more than 2.4% on mAP and 4.2% on AP50. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cui_Feature_Aggregated_Queries_for_Transformer-Based_Video_Object_Detectors_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08319 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Feature_Aggregated_Queries_for_Transformer-Based_Video_Object_Detectors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Feature_Aggregated_Queries_for_Transformer-Based_Video_Object_Detectors_CVPR_2023_paper.html | CVPR 2023 | null |
Context-Aware Pretraining for Efficient Blind Image Decomposition | Chao Wang, Zhedong Zheng, Ruijie Quan, Yifan Sun, Yi Yang | In this paper, we study Blind Image Decomposition (BID), which is to uniformly remove multiple types of degradation at once without foreknowing the noise type. There remain two practical challenges: (1) Existing methods typically require massive data supervision, making them infeasible to real-world scenarios. (2) The conventional paradigm usually focuses on mining the abnormal pattern of a superimposed image to separate the noise, which de facto conflicts with the primary image restoration task. Therefore, such a pipeline compromises repairing efficiency and authenticity. In an attempt to solve the two challenges in one go, we propose an efficient and simplified paradigm, called Context-aware Pretraining (CP), with two pretext tasks: mixed image separation and masked image reconstruction. Such a paradigm reduces the annotation demands and explicitly facilitates context-aware feature learning. Assuming the restoration process follows a structure-to-texture manner, we also introduce a Context-aware Pretrained network (CPNet). In particular, CPNet contains two transformer-based parallel encoders, one information fusion module, and one multi-head prediction module. The information fusion module explicitly utilizes the mutual correlation in the spatial-channel dimension, while the multi-head prediction module facilitates texture-guided appearance flow. Moreover, a new sampling loss along with an attribute label constraint is also deployed to make use of the spatial context, leading to high-fidelity image restoration. Extensive experiments on both real and synthetic benchmarks show that our method achieves competitive performance for various BID tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Context-Aware_Pretraining_for_Efficient_Blind_Image_Decomposition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Context-Aware_Pretraining_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Context-Aware_Pretraining_for_Efficient_Blind_Image_Decomposition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Context-Aware_Pretraining_for_Efficient_Blind_Image_Decomposition_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Posture Mining for Fine-Grained Classification | Zhenchao Tang, Hualin Yang, Calvin Yu-Chian Chen | Because the subtle differences between the different sub-categories of common visual categories such as bird species, fine-grained classification has been seen as a challenging task for many years. Most previous works focus towards the features in the single discriminative region isolatedly, while neglect the connection between the different discriminative regions in the whole image. However, the relationship between different discriminative regions contains rich posture information and by adding the posture information, model can learn the behavior of the object which attribute to improve the classification performance. In this paper, we propose a novel fine-grained framework named PMRC (posture mining and reverse cross-entropy), which is able to combine with different backbones to good effect. In PMRC, we use the Deep Navigator to generate the discriminative regions from the images, and then use them to construct the graph. We aggregate the graph by message passing and get the classification results. Specifically, in order to force PMRC to learn how to mine the posture information, we design a novel training paradigm, which makes the Deep Navigator and message passing communicate and train together. In addition, we propose the reverse cross-entropy (RCE) and demomenstate that compared to the cross-entropy (CE), RCE can not only promote the accurracy of our model but also generalize to promote the accuracy of other kinds of fine-grained classification models. Experimental results on benchmark datasets confirm that PMRC can achieve state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Weakly_Supervised_Posture_Mining_for_Fine-Grained_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Weakly_Supervised_Posture_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Weakly_Supervised_Posture_Mining_for_Fine-Grained_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Weakly_Supervised_Posture_Mining_for_Fine-Grained_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
LAVENDER: Unifying Video-Language Understanding As Masked Language Modeling | Linjie Li, Zhe Gan, Kevin Lin, Chung-Ching Lin, Zicheng Liu, Ce Liu, Lijuan Wang | Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence generation. However, existing video-language (VidL) models still require task-specific designs in model architecture and training objectives for each task. In this work, we explore a unified VidL framework LAVENDER, where Masked Language Modeling (MLM) is used as the common interface for all pre-training and downstream tasks. Such unification leads to a simplified model architecture, where only a lightweight MLM head, instead of a decoder with much more parameters, is needed on top of the multimodal encoder. Surprisingly, experimental results show that this unified framework achieves competitive performance on 14 VidL benchmarks, covering video question answering, text-to-video retrieval and video captioning. Extensive analyses further demonstrate LAVENDER can (i) seamlessly support all downstream tasks with just a single set of parameter values when multi-task finetuned; (ii) generalize to various downstream tasks with limited training samples; and (iii) enable zero-shot evaluation on video question answering tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_LAVENDER_Unifying_Video-Language_Understanding_As_Masked_Language_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_LAVENDER_Unifying_Video-Language_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.07160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_LAVENDER_Unifying_Video-Language_Understanding_As_Masked_Language_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_LAVENDER_Unifying_Video-Language_Understanding_As_Masked_Language_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Decomposed Cross-Modal Distillation for RGB-Based Temporal Action Detection | Pilhyeon Lee, Taeoh Kim, Minho Shim, Dongyoon Wee, Hyeran Byun | Temporal action detection aims to predict the time intervals and the classes of action instances in the video. Despite the promising performance, existing two-stream models exhibit slow inference speed due to their reliance on computationally expensive optical flow. In this paper, we introduce a decomposed cross-modal distillation framework to build a strong RGB-based detector by transferring knowledge of the motion modality. Specifically, instead of direct distillation, we propose to separately learn RGB and motion representations, which are in turn combined to perform action localization. The dual-branch design and the asymmetric training objectives enable effective motion knowledge transfer while preserving RGB information intact. In addition, we introduce a local attentive fusion to better exploit the multimodal complementarity. It is designed to preserve the local discriminability of the features that is important for action localization. Extensive experiments on the benchmarks verify the effectiveness of the proposed method in enhancing RGB-based action detectors. Notably, our framework is agnostic to backbones and detection heads, bringing consistent gains across different model combinations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Decomposed_Cross-Modal_Distillation_for_RGB-Based_Temporal_Action_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.17285 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Decomposed_Cross-Modal_Distillation_for_RGB-Based_Temporal_Action_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Decomposed_Cross-Modal_Distillation_for_RGB-Based_Temporal_Action_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
PyramidFlow: High-Resolution Defect Contrastive Localization Using Pyramid Normalizing Flow | Jiarui Lei, Xiaobo Hu, Yue Wang, Dong Liu | During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in low-resolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lei_PyramidFlow_High-Resolution_Defect_Contrastive_Localization_Using_Pyramid_Normalizing_Flow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lei_PyramidFlow_High-Resolution_Defect_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02595 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_PyramidFlow_High-Resolution_Defect_Contrastive_Localization_Using_Pyramid_Normalizing_Flow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_PyramidFlow_High-Resolution_Defect_Contrastive_Localization_Using_Pyramid_Normalizing_Flow_CVPR_2023_paper.html | CVPR 2023 | null |
On-the-Fly Category Discovery | Ruoyi Du, Dongliang Chang, Kongming Liang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma | Although machines have surpassed humans on visual recognition problems, they are still limited to providing closed-set answers. Unlike machines, humans can cognize novel categories at the first observation. Novel category discovery (NCD) techniques, transferring knowledge from seen categories to distinguish unseen categories, aim to bridge the gap. However, current NCD methods assume a transductive learning and offline inference paradigm, which restricts them to a pre-defined query set and renders them unable to deliver instant feedback. In this paper, we study on-the-fly category discovery (OCD) aimed at making the model instantaneously aware of novel category samples (i.e., enabling inductive learning and streaming inference). We first design a hash coding-based expandable recognition model as a practical baseline. Afterwards, noticing the sensitivity of hash codes to intra-category variance, we further propose a novel Sign-Magnitude dIsentangLEment (SMILE) architecture to alleviate the disturbance it brings. Our experimental results demonstrate the superiority of SMILE against our baseline model and prior art. Our code will be made publicly available. Our code is available at https://github.com/PRIS-CV/On-the-fly-Category-Discovery. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_On-the-Fly_Category_Discovery_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_On-the-Fly_Category_Discovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_On-the-Fly_Category_Discovery_CVPR_2023_paper.html | CVPR 2023 | null |
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models | Yizhuo Chen, Kaizhao Liang, Zhe Zeng, Shuochao Yao, Huajie Shao | Knowledge distillation (KD) is a technique that transfers the knowledge from a large teacher network to a small student network. It has been widely applied to many different tasks, such as model compression and federated learning. However, existing KD methods fail to generalize to general deep directed graphical models (DGMs) with arbitrary layers of random variables. We refer by deep DGMs to DGMs whose conditional distributions are parameterized by deep neural networks. In this work, we propose a novel unified knowledge distillation framework for deep DGMs on various applications. Specifically, we leverage the reparameterization trick to hide the intermediate latent variables, resulting in a compact DGM. Then we develop a surrogate distillation loss to reduce error accumulation through multiple layers of random variables. Moreover, we present the connections between our method and some existing knowledge distillation approaches. The proposed framework is evaluated on four applications: data-free hierarchical variational autoencoder (VAE) compression, data-free variational recurrent neural networks (VRNN) compression, data-free Helmholtz Machine (HM) compression, and VAE continual learning. The results show that our distillation method outperforms the baselines in data-free model compression tasks. We further demonstrate that our method significantly improves the performance of KD-based continual learning for data generation. Our source code is available at https://github.com/YizhuoChen99/KD4DGM-CVPR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_A_Unified_Knowledge_Distillation_Framework_for_Deep_Directed_Graphical_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_A_Unified_Knowledge_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_A_Unified_Knowledge_Distillation_Framework_for_Deep_Directed_Graphical_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_A_Unified_Knowledge_Distillation_Framework_for_Deep_Directed_Graphical_Models_CVPR_2023_paper.html | CVPR 2023 | null |
MAIR: Multi-View Attention Inverse Rendering With 3D Spatially-Varying Lighting Estimation | JunYong Choi, SeokYeong Lee, Haesol Park, Seung-Won Jung, Ig-Jae Kim, Junghyun Cho | We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_MAIR_Multi-View_Attention_Inverse_Rendering_With_3D_Spatially-Varying_Lighting_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_MAIR_Multi-View_Attention_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_MAIR_Multi-View_Attention_Inverse_Rendering_With_3D_Spatially-Varying_Lighting_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_MAIR_Multi-View_Attention_Inverse_Rendering_With_3D_Spatially-Varying_Lighting_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
DF-Platter: Multi-Face Heterogeneous Deepfake Dataset | Kartik Narayan, Harsh Agarwal, Kartik Thakral, Surbhi Mittal, Mayank Vatsa, Richa Singh | Deepfake detection is gaining significant importance in the research community. While most of the research efforts are focused around high-quality images and videos, deepfake generation algorithms today have the capability to generate low-resolution videos, occluded deepfakes, and multiple-subject deepfakes. In this research, we emulate the real-world scenario of deepfake generation and spreading, and propose the DF-Platter dataset, which contains (i) both low-resolution and high-resolution deepfakes generated using multiple generation techniques and (ii) single-subject and multiple-subject deepfakes, with face images of Indian ethnicity. Faces in the dataset are annotated for various attributes such as gender, age, skin tone, and occlusion. The database is prepared in 116 days with continuous usage of 32 GPUs accounting to 1,800 GB cumulative memory. With over 500 GBs in size, the dataset contains a total of 133,260 videos encompassing three sets. To the best of our knowledge, this is one of the largest datasets containing vast variability and multiple challenges. We also provide benchmark results under multiple evaluation settings using popular and state-of-the-art deepfake detection models. Further, benchmark results under c23 and c40 compression are provided. The results demonstrate a significant performance reduction in the deepfake detection task on low-resolution deepfakes and show that the existing techniques fail drastically on multiple-subject deepfakes. It is our assertion that this database will improve the state-of-the-art by extending the capabilities of deepfake detection algorithms to real-world scenarios. The database is available at: http://iab-rubric.org/df-platter-database. | https://openaccess.thecvf.com/content/CVPR2023/papers/Narayan_DF-Platter_Multi-Face_Heterogeneous_Deepfake_Dataset_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Narayan_DF-Platter_Multi-Face_Heterogeneous_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Narayan_DF-Platter_Multi-Face_Heterogeneous_Deepfake_Dataset_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Narayan_DF-Platter_Multi-Face_Heterogeneous_Deepfake_Dataset_CVPR_2023_paper.html | CVPR 2023 | null |
Shifted Diffusion for Text-to-Image Generation | Yufan Zhou, Bingchen Liu, Yizhe Zhu, Xiao Yang, Changyou Chen, Jinhui Xu | We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input text. Different from the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion. Compared to the strong DALL-E 2 baseline, our method performs better in generating image embedding from the text in terms of both efficiency and effectiveness, which consequently results in better text-to-image generation. Extensive large-scale experiments are conducted and evaluated in terms of both quantitative measures and human evaluation, indicating a stronger generation ability of our method compared to existing ones. Furthermore, our model enables semi-supervised and language-free training for text-to-image generation, where only part or none of the images in the training dataset have an associated caption. Trained with only 1.7% of the images being captioned, our semi-supervised model obtains FID results comparable to DALL-E 2 on zero-shot text-to-image generation evaluated on MS-COCO. Corgi also achieves new state-of-the-art results across different datasets on downstream language-free text-to-image generation tasks, outperforming the previous method, Lafite, by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Shifted_Diffusion_for_Text-to-Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Shifted_Diffusion_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15388 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Shifted_Diffusion_for_Text-to-Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Shifted_Diffusion_for_Text-to-Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Unsupervised StyleGAN Image Restoration | Yohan Poirier-Ginter, Jean-François Lalonde | GAN-based image restoration inverts the generative process to repair images corrupted by known degradations. Existing unsupervised methods must carefully be tuned for each task and degradation level. In this work, we make StyleGAN image restoration robust: a single set of hyperparameters works across a wide range of degradation levels. This makes it possible to handle combinations of several degradations, without the need to retune. Our proposed approach relies on a 3-phase progressive latent space extension and a conservative optimizer, which avoids the need for any additional regularization terms. Extensive experiments demonstrate robustness on inpainting, upsampling, denoising, and deartifacting at varying degradations levels, outperforming other StyleGAN-based inversion techniques. Our approach also favorably compares to diffusion-based restoration by yielding much more realistic inversion results. Code will be released upon publication. | https://openaccess.thecvf.com/content/CVPR2023/papers/Poirier-Ginter_Robust_Unsupervised_StyleGAN_Image_Restoration_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.06733 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Poirier-Ginter_Robust_Unsupervised_StyleGAN_Image_Restoration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Poirier-Ginter_Robust_Unsupervised_StyleGAN_Image_Restoration_CVPR_2023_paper.html | CVPR 2023 | null |
Blemish-Aware and Progressive Face Retouching With Limited Paired Data | Lianxin Xie, Wen Xue, Zhen Xu, Si Wu, Zhiwen Yu, Hau San Wong | Face retouching aims to remove facial blemishes, while at the same time maintaining the textual details of a given input image. The main challenge lies in distinguishing blemishes from the facial characteristics, such as moles. Training an image-to-image translation network with pixel-wise supervision suffers from the problem of expensive paired training data, since professional retouching needs specialized experience and is time-consuming. In this paper, we propose a Blemish-aware and Progressive Face Retouching model, which is referred to as BPFRe. Our framework can be partitioned into two manageable stages to perform progressive blemish removal. Specifically, an encoder-decoder-based module learns to coarsely remove the blemishes at the first stage, and the resulting intermediate features are injected into a generator to enrich local detail at the second stage. We find that explicitly suppressing the blemishes can contribute to an effective collaboration among the components. Toward this end, we incorporate an attention module, which learns to infer a blemish-aware map and further determine the corresponding weights, which are then used to refine the intermediate features transferred from the encoder to the decoder, and from the decoder to the generator. Therefore, BPFRe is able to deliver significant performance gains on a wide range of face retouching tasks. It is worth noting that we reduce the dependence of BPFRe on paired training samples by imposing effective regularization on unpaired ones. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Blemish-Aware_and_Progressive_Face_Retouching_With_Limited_Paired_Data_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Blemish-Aware_and_Progressive_Face_Retouching_With_Limited_Paired_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Blemish-Aware_and_Progressive_Face_Retouching_With_Limited_Paired_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Event-Based Frame Interpolation With Ad-Hoc Deblurring | Lei Sun, Christos Sakaridis, Jingyun Liang, Peng Sun, Jiezhang Cao, Kai Zhang, Qi Jiang, Kaiwei Wang, Luc Van Gool | The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolated, depending on the length of the exposure time of the frames and the speed of the motion, and assume either that the input video is sharp, restricting themselves to frame interpolation, or that it is blurry, including an explicit, separate deblurring stage before interpolation in their pipeline. We instead propose a general method for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that naturally incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. In addition, we introduce a novel real-world high-resolution dataset with events and color videos which provides a challenging evaluation setting for the examined task. Extensive experiments on the standard GoPro benchmark and on our dataset show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring and the joint task of interpolation and deblurring. Our code and dataset will be available at https://github.com/AHupuJR/REFID. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Event-Based_Frame_Interpolation_With_Ad-Hoc_Deblurring_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Event-Based_Frame_Interpolation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05191 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Event-Based_Frame_Interpolation_With_Ad-Hoc_Deblurring_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Event-Based_Frame_Interpolation_With_Ad-Hoc_Deblurring_CVPR_2023_paper.html | CVPR 2023 | null |
OvarNet: Towards Open-Vocabulary Object Attribute Recognition | Keyan Chen, Xiaolong Jiang, Yao Hu, Xu Tang, Yan Gao, Jianqi Chen, Weidi Xie | In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario. To achieve this goal, we make the following contributions: (i) we start with a naive two-stage approach for open-vocabulary object detection and attribute classification, termed CLIP-Attr. The candidate objects are first proposed with an offline RPN and later classified for semantic category and attributes; (ii) we combine all available datasets and train with a federated strategy to finetune the CLIP model, aligning the visual representation with attributes, additionally, we investigate the efficacy of leveraging freely available online image-caption pairs under weakly supervised learning; (iii) in pursuit of efficiency, we train a Faster-RCNN type model end-to-end with knowledge distillation, that performs class-agnostic object proposals and classification on semantic categories and attributes with classifiers generated from a text encoder; Finally, (iv) we conduct extensive experiments on VAW, MS-COCO, LSA, and OVAD datasets, and show that recognition of semantic category and attributes is complementary for visual scene understanding, i.e., jointly training object detection and attributes prediction largely outperform existing approaches that treat the two tasks independently, demonstrating strong generalization ability to novel attributes and categories. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_OvarNet_Towards_Open-Vocabulary_Object_Attribute_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_OvarNet_Towards_Open-Vocabulary_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.09506 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_OvarNet_Towards_Open-Vocabulary_Object_Attribute_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_OvarNet_Towards_Open-Vocabulary_Object_Attribute_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Detecting and Grounding Multi-Modal Media Manipulation | Rui Shao, Tianxing Wu, Ziwei Liu | Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. While various deepfake detection and text fake news detection methods have been proposed, they are only designed for single-modality forgery based on binary classification, let alone analyzing and reasoning subtle forgery traces across different modalities. In this paper, we highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM^4). DGM^4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content (i.e., image bounding boxes and text tokens), which requires deeper reasoning of multi-modal media manipulation. To support a large-scale investigation, we construct the first DGM^4 dataset, where image-text pairs are manipulated by various approaches, with rich annotation of diverse manipulations. Moreover, we propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities. HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning. Dedicated manipulation detection and grounding heads are integrated from shallow to deep levels based on the interacted multi-modal information. Finally, we build an extensive benchmark and set up rigorous evaluation metrics for this new research problem. Comprehensive experiments demonstrate the superiority of our model; several valuable observations are also revealed to facilitate future research in multi-modal media manipulation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shao_Detecting_and_Grounding_Multi-Modal_Media_Manipulation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shao_Detecting_and_Grounding_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Detecting_and_Grounding_Multi-Modal_Media_Manipulation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Detecting_and_Grounding_Multi-Modal_Media_Manipulation_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Detection in Crowd Analysis via Underutilized Output Features | Shaokai Wu, Fengyu Yang | Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. However, we argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. Specifically, the area size and confidence score of output proposals and bounding boxes provide insight into the scale and density of the crowd. To leverage these underutilized features, we propose Crowd Hat, a plug-and-play module that can be easily integrated with existing detection models. This module uses a mixed 2D-1D compression technique to refine the output features and obtain the spatial and numerical distribution of crowd-specific information. Based on these features, we further propose region-adaptive NMS thresholds and a decouple-then-align paradigm that address the major limitations of detection-based methods. Our extensive evaluations on various crowd analysis tasks, including crowd counting, localization, and detection, demonstrate the effectiveness of utilizing output features and the potential of detection-based methods in crowd analysis. Our code is available at https://github.com/wskingdom/Crowd-Hat. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Boosting_Detection_in_Crowd_Analysis_via_Underutilized_Output_Features_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Boosting_Detection_in_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Boosting_Detection_in_Crowd_Analysis_via_Underutilized_Output_Features_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Boosting_Detection_in_Crowd_Analysis_via_Underutilized_Output_Features_CVPR_2023_paper.html | CVPR 2023 | null |
Human Pose As Compositional Tokens | Zigang Geng, Chunyu Wang, Yixuan Wei, Ze Liu, Houqiang Li, Han Hu | Human pose is typically represented by a coordinate vector of body joints or their heatmap embeddings. While easy for data processing, unrealistic pose estimates are admitted due to the lack of dependency modeling between the body joints. In this paper, we present a structured representation, named Pose as Compositional Tokens (PCT), to explore the joint dependency. It represents a pose by M discrete tokens with each characterizing a sub-structure with several interdependent joints. The compositional design enables it to achieve a small reconstruction error at a low cost. Then we cast pose estimation as a classification task. In particular, we learn a classifier to predict the categories of the M tokens from an image. A pre-learned decoder network is used to recover the pose from the tokens without further post-processing. We show that it achieves better or comparable pose estimation results as the existing methods in general scenarios, yet continues to work well when occlusion occurs, which is ubiquitous in practice. The code and models are publicly available at https://github.com/Gengzigang/PCT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Geng_Human_Pose_As_Compositional_Tokens_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Geng_Human_Pose_As_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_Human_Pose_As_Compositional_Tokens_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_Human_Pose_As_Compositional_Tokens_CVPR_2023_paper.html | CVPR 2023 | null |
K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring | Yan Yang, Liyuan Pan, Liu Liu, Miaomiao Liu | The dual-pixel (DP) sensor captures a two-view image pair in a single snapshot by splitting each pixel in half. The disparity occurs in defocus blurred regions between the two views of the DP pair, while the in-focus sharp regions have zero disparity. This motivates us to propose a K3DN framework for DP pair deblurring, and it has three modules: i) a disparity-aware deblur module. It estimates a disparity feature map, which is used to query a trainable kernel set to estimate a blur kernel that best describes the spatially-varying blur. The kernel is constrained to be symmetrical per the DP formulation. A simple Fourier transform is performed for deblurring that follows the blur model; ii) a reblurring regularization module. It reuses the blur kernel, performs a simple convolution for reblurring, and regularizes the estimated kernel and disparity feature unsupervisedly, in the training stage; iii) a sharp region preservation module. It identifies in-focus regions that correspond to areas with zero disparity between DP images, aims to avoid the introduction of noises during the deblurring process, and improves image restoration performance. Experiments on four standard DP datasets show that the proposed K3DN outperforms state-of-the-art methods, with fewer parameters and flops at the same time. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_K3DN_Disparity-Aware_Kernel_Estimation_for_Dual-Pixel_Defocus_Deblurring_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_K3DN_Disparity-Aware_Kernel_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_K3DN_Disparity-Aware_Kernel_Estimation_for_Dual-Pixel_Defocus_Deblurring_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_K3DN_Disparity-Aware_Kernel_Estimation_for_Dual-Pixel_Defocus_Deblurring_CVPR_2023_paper.html | CVPR 2023 | null |
3D Line Mapping Revisited | Shaohui Liu, Yifan Yu, Rémi Pautrat, Marc Pollefeys, Viktor Larsson | In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements. In addition to offering strong geometric cues, they are also omnipresent in urban landscapes and indoor scenes. Despite their apparent advantages, current line-based reconstruction methods are far behind their point-based counterparts. In this paper we aim to close the gap by introducing LIMAP, a library for 3D line mapping that robustly and efficiently creates 3D line maps from multi-view imagery. This is achieved through revisiting the degeneracy problem of line triangulation, carefully crafted scoring and track building, and exploiting structural priors such as line coincidence, parallelism, and orthogonality. Our code integrates seamlessly with existing point-based Structure-from-Motion methods and can leverage their 3D points to further improve the line reconstruction. Furthermore, as a byproduct, the method is able to recover 3D association graphs between lines and points / vanishing points (VPs). In thorough experiments, we show that LIMAP significantly outperforms existing approaches for 3D line mapping. Our robust 3D line maps also open up new research directions. We show two example applications: visual localization and bundle adjustment, where integrating lines alongside points yields the best results. Code is available at https://github.com/cvg/limap. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_3D_Line_Mapping_Revisited_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_3D_Line_Mapping_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17504 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_3D_Line_Mapping_Revisited_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_3D_Line_Mapping_Revisited_CVPR_2023_paper.html | CVPR 2023 | null |
DartBlur: Privacy Preservation With Detection Artifact Suppression | Baowei Jiang, Bing Bai, Haozhe Lin, Yu Wang, Yuchen Guo, Lu Fang | Nowadays, privacy issue has become a top priority when training AI algorithms. Machine learning algorithms are expected to benefit our daily life, while personal information must also be carefully protected from exposure. Facial information is particularly sensitive in this regard. Multiple datasets containing facial information have been taken offline, and the community is actively seeking solutions to remedy the privacy issues. Existing methods for privacy preservation can be divided into blur-based and face replacement-based methods. Owing to the advantages of review convenience and good accessibility, blur-based based methods have become a dominant choice in practice. However, blur-based methods would inevitably introduce training artifacts harmful to the performance of downstream tasks. In this paper, we propose a novel De-artifact Blurring(DartBlur) privacy-preserving method, which capitalizes on a DNN architecture to generate blurred faces. DartBlur can effectively hide facial privacy information while detection artifacts are simultaneously suppressed. We have designed four training objectives that particularly aim to improve review convenience and maximize detection artifact suppression. We associate the algorithm with an adversarial training strategy with a second-order optimization pipeline. Experimental results demonstrate that DartBlur outperforms the existing face-replacement method from both perspectives of review convenience and accessibility, and also shows an exclusive advantage in suppressing the training artifact compared to traditional blur-based methods. Our implementation is available at https://github.com/JaNg2333/DartBlur. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_DartBlur_Privacy_Preservation_With_Detection_Artifact_Suppression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_DartBlur_Privacy_Preservation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_DartBlur_Privacy_Preservation_With_Detection_Artifact_Suppression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_DartBlur_Privacy_Preservation_With_Detection_Artifact_Suppression_CVPR_2023_paper.html | CVPR 2023 | null |
Synthesizing Photorealistic Virtual Humans Through Cross-Modal Disentanglement | Siddarth Ravichandran, Ondřej Texler, Dimitar Dinev, Hyun Jae Kang | Over the last few decades, many aspects of human life have been enhanced with virtual domains, from the advent of digital assistants such as Amazon's Alexa and Apple's Siri to the latest metaverse efforts of the rebranded Meta. These trends underscore the importance of generating photorealistic visual depictions of humans. This has led to the rapid growth of so-called deepfake and talking-head generation methods in recent years. Despite their impressive results and popularity, they usually lack certain qualitative aspects such as texture quality, lips synchronization, or resolution, and practical aspects such as the ability to run in real-time. To allow for virtual human avatars to be used in practical scenarios, we propose an end-to-end framework for synthesizing high-quality virtual human faces capable of speaking with accurate lip motion with a special emphasis on performance. We introduce a novel network utilizing visemes as an intermediate audio representation and a novel data augmentation strategy employing a hierarchical image synthesis approach that allows disentanglement of the different modalities used to control the global head motion. Our method runs in real-time, and is able to deliver superior results compared to the current state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ravichandran_Synthesizing_Photorealistic_Virtual_Humans_Through_Cross-Modal_Disentanglement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ravichandran_Synthesizing_Photorealistic_Virtual_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2209.01320 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ravichandran_Synthesizing_Photorealistic_Virtual_Humans_Through_Cross-Modal_Disentanglement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ravichandran_Synthesizing_Photorealistic_Virtual_Humans_Through_Cross-Modal_Disentanglement_CVPR_2023_paper.html | CVPR 2023 | null |
Test Time Adaptation With Regularized Loss for Weakly Supervised Salient Object Detection | Olga Veksler | It is well known that CNNs tend to overfit to the training data. Test-time adaptation is an extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained model to that image. Indeed nothing can be closer to the test data than the test image itself. The main difficulty of test-time adaptation is that the ground truth is not available. Thus test-time adaptation, while intriguing, applies to only a few scenarios where one can design an effective loss function that does not require ground truth. We propose the first approach for test-time Salient Object Detection (SOD) in the context of weak supervision. Our approach is based on a so called regularized loss function, which can be used for training CNN when pixel precise ground truth is unavailable. Regularized loss tends to have lower values for the more likely object segments, and thus it can be used to fine-tune an already trained CNN to a given test image, adapting to images unseen during training. We develop a regularized loss function particularly suitable for test-time adaptation and show that our approach significantly outperforms prior work for weakly supervised SOD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Veksler_Test_Time_Adaptation_With_Regularized_Loss_for_Weakly_Supervised_Salient_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Veksler_Test_Time_Adaptation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Veksler_Test_Time_Adaptation_With_Regularized_Loss_for_Weakly_Supervised_Salient_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Veksler_Test_Time_Adaptation_With_Regularized_Loss_for_Weakly_Supervised_Salient_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Pre-Training With Masked Shape Prediction for 3D Scene Understanding | Li Jiang, Zetong Yang, Shaoshuai Shi, Vladislav Golyanik, Dengxin Dai, Bernt Schiele | Masked signal modeling has greatly advanced self-supervised pre-training for language and 2D images. However, it is still not fully explored in 3D scene understanding. Thus, this paper introduces Masked Shape Prediction (MSP), a new framework to conduct masked signal modeling in 3D scenes. MSP uses the essential 3D semantic cue, i.e., geometric shape, as the prediction target for masked points. The context-enhanced shape target consisting of explicit shape context and implicit deep shape feature is proposed to facilitate exploiting contextual cues in shape prediction. Meanwhile, the pre-training architecture in MSP is carefully designed to alleviate the masked shape leakage from point coordinates. Experiments on multiple 3D understanding tasks on both indoor and outdoor datasets demonstrate the effectiveness of MSP in learning good feature representations to consistently boost downstream performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Self-Supervised_Pre-Training_With_Masked_Shape_Prediction_for_3D_Scene_Understanding_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.05026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Self-Supervised_Pre-Training_With_Masked_Shape_Prediction_for_3D_Scene_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Self-Supervised_Pre-Training_With_Masked_Shape_Prediction_for_3D_Scene_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration | Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, Luc Van Gool | The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important properties of natural images including cross-scale similarity and anisotropic image features. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention and the modelling capacity beyond the regional range. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-attention, and channel attention enhanced convolution. Finally, the proposed network is applied to 7 image restoration types, covering both real and synthetic settings. The proposed method sets the new state-of-the-art for several of those. Code will be available at https://github.com/ofsoundof/GRL-Image-Restoration.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Efficient_and_Explicit_Modelling_of_Image_Hierarchies_for_Image_Restoration_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.00748 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Efficient_and_Explicit_Modelling_of_Image_Hierarchies_for_Image_Restoration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Efficient_and_Explicit_Modelling_of_Image_Hierarchies_for_Image_Restoration_CVPR_2023_paper.html | CVPR 2023 | null |
Guiding Pseudo-Labels With Uncertainty Estimation for Source-Free Unsupervised Domain Adaptation | Mattia Litrico, Alessio Del Bue, Pietro Morerio | Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Litrico_Guiding_Pseudo-Labels_With_Uncertainty_Estimation_for_Source-Free_Unsupervised_Domain_Adaptation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.03770 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Litrico_Guiding_Pseudo-Labels_With_Uncertainty_Estimation_for_Source-Free_Unsupervised_Domain_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Litrico_Guiding_Pseudo-Labels_With_Uncertainty_Estimation_for_Source-Free_Unsupervised_Domain_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation | Akash Sengupta, Ignas Budvytis, Roberto Cipolla | Monocular 3D human pose and shape estimation is an ill-posed problem since multiple 3D solutions can explain a 2D image of a subject. Recent approaches predict a probability distribution over plausible 3D pose and shape parameters conditioned on the image. We show that these approaches exhibit a trade-off between three key properties: (i) accuracy - the likelihood of the ground-truth 3D solution under the predicted distribution, (ii) sample-input consistency - the extent to which 3D samples from the predicted distribution match the visible 2D image evidence, and (iii) sample diversity - the range of plausible 3D solutions modelled by the predicted distribution. Our method, HuManiFlow, predicts simultaneously accurate, consistent and diverse distributions. We use the human kinematic tree to factorise full body pose into ancestor-conditioned per-body-part pose distributions in an autoregressive manner. Per-body-part distributions are implemented using normalising flows that respect the manifold structure of SO(3), the Lie group of per-body-part poses. We show that ill-posed, but ubiquitous, 3D point estimate losses reduce sample diversity, and employ only probabilistic training losses. HuManiFlow outperforms state-of-the-art probabilistic approaches on the 3DPW and SSP-3D datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sengupta_HuManiFlow_Ancestor-Conditioned_Normalising_Flows_on_SO3_Manifolds_for_Human_Pose_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sengupta_HuManiFlow_Ancestor-Conditioned_Normalising_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.06968 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sengupta_HuManiFlow_Ancestor-Conditioned_Normalising_Flows_on_SO3_Manifolds_for_Human_Pose_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sengupta_HuManiFlow_Ancestor-Conditioned_Normalising_Flows_on_SO3_Manifolds_for_Human_Pose_CVPR_2023_paper.html | CVPR 2023 | null |
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning | Xinyuan Gao, Yuhang He, Songlin Dong, Jie Cheng, Xing Wei, Yihong Gong | Deep neural networks suffer from catastrophic forgetting in class incremental learning, where the classification accuracy of old classes drastically deteriorates when the networks learn the knowledge of new classes. Many works have been proposed to solve the class incremental learning problem. However, most of them either suffer from serious catastrophic forgetting and stability-plasticity dilemma or need too many extra parameters and computations. To meet the challenge, we propose a novel framework, Diverse Knowledge Transfer Transformer (DKT). which contains two novel knowledge transfers based on the attention mechanism to transfer the task-general knowledge and task-specific knowledge to the current task to alleviate catastrophic forgetting. Besides, we propose a duplex classifier to address the stability-plasticity dilemma, and a novel loss function to cluster the same categories in feature space and discriminate the features between old and new tasks to force the task specific knowledge to be more diverse. Our method needs only a few extra parameters, which are negligible, to tackle the increasing number of tasks. We conduct comprehensive experimental results on CIFAR100, ImageNet100/1000 datasets. The experiment results show that our method outperforms other competitive methods and achieves state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_DKT_Diverse_Knowledge_Transfer_Transformer_for_Class_Incremental_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_DKT_Diverse_Knowledge_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_DKT_Diverse_Knowledge_Transfer_Transformer_for_Class_Incremental_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_DKT_Diverse_Knowledge_Transfer_Transformer_for_Class_Incremental_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
LipFormer: High-Fidelity and Generalizable Talking Face Generation With a Pre-Learned Facial Codebook | Jiayu Wang, Kang Zhao, Shiwei Zhang, Yingya Zhang, Yujun Shen, Deli Zhao, Jingren Zhou | Generating a talking face video from the input audio sequence is a practical yet challenging task. Most existing methods either fail to capture fine facial details or need to train a specific model for each identity. We argue that a codebook pre-learned on high-quality face images can serve as a useful prior that facilitates high-fidelity and generalizable talking head synthesis. Thanks to the strong capability of the codebook in representing face textures, we simplify the talking face generation task as finding proper lip-codes to characterize the variation of lips during a portrait talking. To this end, we propose LipFormer, a transformer-based framework, to model the audio-visual coherence and predict the lip-codes sequence based on the input audio features. We further introduce an adaptive face warping module, which helps warp the reference face to the target pose in the feature space, to alleviate the difficulty of lip-code prediction under different poses. By this means, LipFormer can make better use of the pre-learned priors in images and is robust to posture change. Extensive experiments show that LipFormer can produce more realistic talking face videos compared to previous methods and faithfully generalize to unseen identities. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_LipFormer_High-Fidelity_and_Generalizable_Talking_Face_Generation_With_a_Pre-Learned_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_LipFormer_High-Fidelity_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LipFormer_High-Fidelity_and_Generalizable_Talking_Face_Generation_With_a_Pre-Learned_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LipFormer_High-Fidelity_and_Generalizable_Talking_Face_Generation_With_a_Pre-Learned_CVPR_2023_paper.html | CVPR 2023 | null |
Generalizable Local Feature Pre-Training for Deformable Shape Analysis | Souhaib Attaiki, Lei Li, Maks Ovsjanikov | Transfer learning is fundamental for addressing problems in settings with little training data. While several transfer learning approaches have been proposed in 3D, unfortunately, these solutions typically operate on an entire 3D object or even scene-level and thus, as we show, fail to generalize to new classes, such as deformable organic shapes. In addition, there is currently a lack of understanding of what makes pre-trained features transferable across significantly different 3D shape categories. In this paper, we make a step toward addressing these challenges. First, we analyze the link between feature locality and transferability in tasks involving deformable 3D objects, while also comparing different backbones and losses for local feature pre-training. We observe that with proper training, learned features can be useful in such tasks, but, crucially, only with an appropriate choice of the receptive field size. We then propose a differentiable method for optimizing the receptive field within 3D transfer learning. Jointly, this leads to the first learnable features that can successfully generalize to unseen classes of 3D shapes such as humans and animals. Our extensive experiments show that this approach leads to state-of-the-art results on several downstream tasks such as segmentation, shape correspondence, and classification. Our code is available at https://github.com/pvnieo/vader. | https://openaccess.thecvf.com/content/CVPR2023/papers/Attaiki_Generalizable_Local_Feature_Pre-Training_for_Deformable_Shape_Analysis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Attaiki_Generalizable_Local_Feature_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15104 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Attaiki_Generalizable_Local_Feature_Pre-Training_for_Deformable_Shape_Analysis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Attaiki_Generalizable_Local_Feature_Pre-Training_for_Deformable_Shape_Analysis_CVPR_2023_paper.html | CVPR 2023 | null |
TarViS: A Unified Approach for Target-Based Video Segmentation | Ali Athar, Alexander Hermans, Jonathon Luiten, Deva Ramanan, Bastian Leibe | The general domain of video segmentation is currently fragmented into different tasks spanning multiple benchmarks. Despite rapid progress in the state-of-the-art, current methods are overwhelmingly task-specific and cannot conceptually generalize to other tasks. Inspired by recent approaches with multi-task capability, we propose TarViS: a novel, unified network architecture that can be applied to any task that requires segmenting a set of arbitrarily defined 'targets' in video. Our approach is flexible with respect to how tasks define these targets, since it models the latter as abstract 'queries' which are then used to predict pixel-precise target masks. A single TarViS model can be trained jointly on a collection of datasets spanning different tasks, and can hot-swap between tasks during inference without any task-specific retraining. To demonstrate its effectiveness, we apply TarViS to four different tasks, namely Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), Video Object Segmentation (VOS) and Point Exemplar-guided Tracking (PET). Our unified, jointly trained model achieves state-of-the-art performance on 5/7 benchmarks spanning these four tasks, and competitive performance on the remaining two. Code and model weights are available at: https://github.com/Ali2500/TarViS | https://openaccess.thecvf.com/content/CVPR2023/papers/Athar_TarViS_A_Unified_Approach_for_Target-Based_Video_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Athar_TarViS_A_Unified_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2301.02657 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Athar_TarViS_A_Unified_Approach_for_Target-Based_Video_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Athar_TarViS_A_Unified_Approach_for_Target-Based_Video_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Random Convolutions for Single Domain Generalization | Seokeon Choi, Debasmit Das, Sungha Choi, Seunghan Yang, Hyunsin Park, Sungrack Yun | Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains. Image augmentation based on Random Convolutions (RandConv), consisting of one convolution layer randomly initialized for each mini-batch, enables the model to learn generalizable visual representations by distorting local textures despite its simple and lightweight structure. However, RandConv has structural limitations in that the generated image easily loses semantics as the kernel size increases, and lacks the inherent diversity of a single convolution operation. To solve the problem, we propose a Progressive Random Convolution (Pro-RandConv) method that recursively stacks random convolution layers with a small kernel size instead of increasing the kernel size. This progressive approach can not only mitigate semantic distortions by reducing the influence of pixels away from the center in the theoretical receptive field, but also create more effective virtual domains by gradually increasing the style diversity. In addition, we develop a basic random convolution layer into a random convolution block including deformable offsets and affine transformation to support texture and contrast diversification, both of which are also randomly initialized. Without complex generators or adversarial learning, we demonstrate that our simple yet effective augmentation strategy outperforms state-of-the-art methods on single domain generalization benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Progressive_Random_Convolutions_for_Single_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_Progressive_Random_Convolutions_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Progressive_Random_Convolutions_for_Single_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Progressive_Random_Convolutions_for_Single_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
IDGI: A Framework To Eliminate Explanation Noise From Integrated Gradients | Ruo Yang, Binghui Wang, Mustafa Bilgic | Integrated Gradients (IG) as well as its variants are well-known techniques for interpreting the decisions of deep neural networks. While IG-based approaches attain state-of-the-art performance, they often integrate noise into their explanation saliency maps, which reduce their interpretability. To minimize the noise, we examine the source of the noise analytically and propose a new approach to reduce the explanation noise based on our analytical findings. We propose the Important Direction Gradient Integration (IDGI) framework, which can be easily incorporated into any IG-based method that uses the Reimann Integration for integrated gradient computation. Extensive experiments with three IG-based methods show that IDGI improves them drastically on numerous interpretability metrics. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_IDGI_A_Framework_To_Eliminate_Explanation_Noise_From_Integrated_Gradients_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_IDGI_A_Framework_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_IDGI_A_Framework_To_Eliminate_Explanation_Noise_From_Integrated_Gradients_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_IDGI_A_Framework_To_Eliminate_Explanation_Noise_From_Integrated_Gradients_CVPR_2023_paper.html | CVPR 2023 | null |
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-Free Upsampling Module in Arbitrary-Scale Image Super-Resolution | Gaochao Song, Qian Sun, Luo Zhang, Ran Su, Jianfeng Shi, Ying He | Arbitrary-scale image super-resolution (SR) is often tackled using the implicit neural representation (INR) approach, which relies on a position encoding scheme to improve its representation ability. In this paper, we introduce orthogonal position encoding (OPE), an extension of position encoding, and an OPE-Upscale module to replace the INR-based upsampling module for arbitrary-scale image super-resolution. Our OPE-Upscale module takes 2D coordinates and latent code as inputs, just like INR, but does not require any training parameters. This parameter-free feature allows the OPE-Upscale module to directly perform linear combination operations, resulting in continuous image reconstruction and achieving arbitrary-scale image reconstruction. As a concise SR framework, our method is computationally efficient and consumes less memory than state-of-the-art methods, as confirmed by extensive experiments and evaluations. In addition, our method achieves comparable results with state-of-the-art methods in arbitrary-scale image super-resolution. Lastly, we show that OPE corresponds to a set of orthogonal basis, validating our design principle. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_OPE-SR_Orthogonal_Position_Encoding_for_Designing_a_Parameter-Free_Upsampling_Module_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_OPE-SR_Orthogonal_Position_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_OPE-SR_Orthogonal_Position_Encoding_for_Designing_a_Parameter-Free_Upsampling_Module_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_OPE-SR_Orthogonal_Position_Encoding_for_Designing_a_Parameter-Free_Upsampling_Module_CVPR_2023_paper.html | CVPR 2023 | null |
Implicit Surface Contrastive Clustering for LiDAR Point Clouds | Zaiwei Zhang, Min Bai, Erran Li | Self-supervised pretraining on large unlabeled datasets has shown tremendous success on improving the task performance of many computer vision tasks. However, such techniques have not been widely used for outdoor LiDAR point cloud perception due to its scene complexity and wide range. This prevents impactful application from 2D pretraining frameworks. In this paper, we propose ISCC, a new self-supervised pretraining method, core of which are two pretext tasks newly designed for LiDAR point clouds. The first task focuses on learning semantic information by sorting local groups of points in the scene into a globally consistent set of semantically meaningful clusters using contrastive learning. This is augmented with a second task which reasons about precise surfaces of various parts of the scene through implicit surface reconstruction to learn geometric structures. We demonstrate their effectiveness on transfer learning performance on 3D object detection and semantic segmentation in real world LiDAR scenes. We further design an unsupervised semantic grouping task to showcase the highly semantically meaningful features learned by our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Implicit_Surface_Contrastive_Clustering_for_LiDAR_Point_Clouds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Implicit_Surface_Contrastive_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Implicit_Surface_Contrastive_Clustering_for_LiDAR_Point_Clouds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Implicit_Surface_Contrastive_Clustering_for_LiDAR_Point_Clouds_CVPR_2023_paper.html | CVPR 2023 | null |
EC2: Emergent Communication for Embodied Control | Yao Mu, Shunyu Yao, Mingyu Ding, Ping Luo, Chuang Gan | Embodied control requires agents to leverage multi-modal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC^2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC^2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mu_EC2_Emergent_Communication_for_Embodied_Control_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mu_EC2_Emergent_Communication_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mu_EC2_Emergent_Communication_for_Embodied_Control_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mu_EC2_Emergent_Communication_for_Embodied_Control_CVPR_2023_paper.html | CVPR 2023 | null |
Semantic Ray: Learning a Generalizable Semantic Field With Cross-Reprojection Attention | Fangfu Liu, Chubin Zhang, Yu Zheng, Yueqi Duan | In this paper, we aim to learn a semantic radiance field from multiple scenes that is accurate, efficient and generalizable. While most existing NeRFs target at the tasks of neural scene rendering, image synthesis and multi-view reconstruction, there are a few attempts such as Semantic-NeRF that explore to learn high-level semantic understanding with the NeRF structure. However, Semantic-NeRF simultaneously learns color and semantic label from a single ray with multiple heads, where the single ray fails to provide rich semantic information. As a result, Semantic NeRF relies on positional encoding and needs to train one specific model for each scene. To address this, we propose Semantic Ray (S-Ray) to fully exploit semantic information along the ray direction from its multi-view reprojections. As directly performing dense attention over multi-view reprojected rays would suffer from heavy computational cost, we design a Cross-Reprojection Attention module with consecutive intra-view radial and cross-view sparse attentions, which decomposes contextual information along reprojected rays and cross multiple views and then collects dense connections by stacking the modules. Experiments show that our S-Ray is able to learn from multiple scenes, and it presents strong generalization ability to adapt to unseen scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Semantic_Ray_Learning_a_Generalizable_Semantic_Field_With_Cross-Reprojection_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Semantic_Ray_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Semantic_Ray_Learning_a_Generalizable_Semantic_Field_With_Cross-Reprojection_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Semantic_Ray_Learning_a_Generalizable_Semantic_Field_With_Cross-Reprojection_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
DynamicDet: A Unified Dynamic Architecture for Object Detection | Zhihao Lin, Yongtao Wang, Jinhe Zhang, Xiaojie Chu | Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However, it is challenging to design a powerful dynamic detector, because of no suitable dynamic architecture and exiting criterion for object detection. To tackle these difficulties, we propose a dynamic framework for object detection, named DynamicDet. Firstly, we carefully design a dynamic architecture based on the nature of the object detection task. Then, we propose an adaptive router to analyze the multi-scale information and to decide the inference route automatically. We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors. Last, we present a variable-speed inference strategy, which helps to realize a wide range of accuracy-speed trade-offs with only one dynamic detector. Extensive experiments conducted on the COCO benchmark demonstrate that the proposed DynamicDet achieves new state-of-the-art accuracy-speed trade-offs. For instance, with comparable accuracy, the inference speed of our dynamic detector Dy-YOLOv7-W6 surpasses YOLOv7-E6 by 12%, YOLOv7-D6 by 17%, and YOLOv7-E6E by 39%. The code is available at https://github.com/VDIGPKU/DynamicDet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_DynamicDet_A_Unified_Dynamic_Architecture_for_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_DynamicDet_A_Unified_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_DynamicDet_A_Unified_Dynamic_Architecture_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_DynamicDet_A_Unified_Dynamic_Architecture_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification | Muhammad Ferjad Naeem, Muhammad Gul Zain Ali Khan, Yongqin Xian, Muhammad Zeshan Afzal, Didier Stricker, Luc Van Gool, Federico Tombari | Recent works have shown that unstructured text (documents) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods require access to a high-quality source like Wikipedia and are limited to a single source of information. Large Language Models (LLM) trained on web-scale text show impressive abilities to repurpose their learned knowledge for a multitude of tasks. In this work, we provide a novel perspective on using an LLM to provide text supervision for a zero-shot image classification model. The LLM is provided with a few text descriptions from different annotators as examples. The LLM is conditioned on these examples to generate multiple text descriptions for each class (referred to as views). Our proposed model, I2MVFormer, learns multi-view semantic embeddings for zero-shot image classification with these class views. We show that each text view of a class provides complementary information allowing a model to learn a highly discriminative class embedding. Moreover, we show that I2MVFormer is better at consuming the multi-view text supervision from LLM compared to baseline models. I2MVFormer establishes a new state-of-the-art on three public benchmark datasets for zero-shot image classification with unsupervised semantic embeddings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Naeem_I2MVFormer_Large_Language_Model_Generated_Multi-View_Document_Supervision_for_Zero-Shot_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Naeem_I2MVFormer_Large_Language_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02291 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Naeem_I2MVFormer_Large_Language_Model_Generated_Multi-View_Document_Supervision_for_Zero-Shot_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Naeem_I2MVFormer_Large_Language_Model_Generated_Multi-View_Document_Supervision_for_Zero-Shot_CVPR_2023_paper.html | CVPR 2023 | null |
MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation | Simon Suo, Kelvin Wong, Justin Xu, James Tu, Alexander Cui, Sergio Casas, Raquel Urtasun | The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the real world, we need to evaluate them in closed-loop. Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations. In particular, we present MixSim, a hierarchical framework for mixed reality traffic simulation. MixSim explicitly models agent goals as routes along the road network and learns a reactive route-conditional policy. By inferring each agent's route from the original scenario, MixSim can reactively re-simulate the scenario and enable testing different autonomy systems under the same conditions. Furthermore, by varying each agent's route, we can expand the scope of testing to what-if situations with realistic variations in agent behaviors or even safety-critical interactions. Our experiments show that MixSim can serve as a realistic, reactive, and controllable digital twin of real world scenarios. For more information, please visit the project website: https://waabi.ai/research/mixsim/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Suo_MixSim_A_Hierarchical_Framework_for_Mixed_Reality_Traffic_Simulation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Suo_MixSim_A_Hierarchical_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Suo_MixSim_A_Hierarchical_Framework_for_Mixed_Reality_Traffic_Simulation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Suo_MixSim_A_Hierarchical_Framework_for_Mixed_Reality_Traffic_Simulation_CVPR_2023_paper.html | CVPR 2023 | null |
ORCa: Glossy Objects As Radiance-Field Cameras | Kushagra Tiwary, Akshat Dave, Nikhil Behari, Tzofi Klinghoffer, Ashok Veeraraghavan, Ramesh Raskar | Reflections on glossy objects contain valuable and hidden information about the surrounding environment. By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e.g. from reflections on the human eye. However, this task is challenging because reflections depend jointly on object geometry, material properties, the 3D environment, and the observer's viewing direction. Our approach converts glossy objects with unknown geometry into radiance-field cameras to image the world from the object's perspective. Our key insight is to convert the object surface into a virtual sensor that captures cast reflections as a 2D projection of the 5D environment radiance field visible to and surrounding the object. We show that recovering the environment radiance fields enables depth and radiance estimation from the object to its surroundings in addition to beyond field-of-view novel-view synthesis, i.e. rendering of novel views that are only directly visible to the glossy object present in the scene, but not the observer. Moreover, using the radiance field we can image around occluders caused by close-by objects in the scene. Our method is trained end-to-end on multi-view images of the object and jointly estimates object geometry, diffuse radiance, and the 5D environment radiance field. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tiwary_ORCa_Glossy_Objects_As_Radiance-Field_Cameras_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tiwary_ORCa_Glossy_Objects_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tiwary_ORCa_Glossy_Objects_As_Radiance-Field_Cameras_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tiwary_ORCa_Glossy_Objects_As_Radiance-Field_Cameras_CVPR_2023_paper.html | CVPR 2023 | null |
SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations | Pu Li, Jianwei Guo, Xiaopeng Zhang, Dong-Ming Yan | Reverse engineering CAD models from raw geometry is a classic but strenuous research problem. Previous learning-based methods rely heavily on labels due to the supervised design patterns or reconstruct CAD shapes that are not easily editable. In this work, we introduce SECAD-Net, an end-to-end neural network aimed at reconstructing compact and easy-to-edit CAD models in a self-supervised manner. Drawing inspiration from the modeling language that is most commonly used in modern CAD software, we propose to learn 2D sketches and 3D extrusion parameters from raw shapes, from which a set of extrusion cylinders can be generated by extruding each sketch from a 2D plane into a 3D body. By incorporating the Boolean operation (i.e., union), these cylinders can be combined to closely approximate the target geometry. We advocate the use of implicit fields for sketch representation, which allows for creating CAD variations by interpolating latent codes in the sketch latent space. Extensive experiments on both ABC and Fusion 360 datasets demonstrate the effectiveness of our method, and show superiority over state-of-the-art alternatives including the closely related method for supervised CAD reconstruction. We further apply our approach to CAD editing and single-view CAD reconstruction. The code is released at https://github.com/BunnySoCrazy/SECAD-Net. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SECAD-Net_Self-Supervised_CAD_Reconstruction_by_Learning_Sketch-Extrude_Operations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_SECAD-Net_Self-Supervised_CAD_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SECAD-Net_Self-Supervised_CAD_Reconstruction_by_Learning_Sketch-Extrude_Operations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SECAD-Net_Self-Supervised_CAD_Reconstruction_by_Learning_Sketch-Extrude_Operations_CVPR_2023_paper.html | CVPR 2023 | null |
Context-Aware Alignment and Mutual Masking for 3D-Language Pre-Training | Zhao Jin, Munawar Hayat, Yuwei Yang, Yulan Guo, Yinjie Lei | 3D visual language reasoning plays an important role in effective human-computer interaction. The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks. Despite the encouraging progress in vision-language pre-training for image-text data, 3D-language pre-training is still an open issue due to limited 3D-language paired data, highly sparse and irregular structure of point clouds and ambiguities in spatial relations of 3D objects with viewpoint changes. In this paper, we present a generic 3D-language pre-training approach, that tackles multiple facets of 3D-language reasoning by learning universal representations. Our learning objective constitutes two main parts. 1) Context aware spatial-semantic alignment to establish fine-grained correspondence between point clouds and texts. It reduces relational ambiguities by aligning 3D spatial relationships with textual semantic context. 2) Mutual 3D-Language Masked modeling to enable cross-modality information exchange. Instead of reconstructing sparse 3D points for which language can hardly provide cues, we propose masked proposal reasoning to learn semantic class and mask-invariant representations. Our proposed 3D-language pre-training method achieves promising results once adapted to various downstream tasks, including 3D visual grounding, 3D dense captioning and 3D question answering. Our codes are available at https://github.com/leolyj/3D-VLP | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Context-Aware_Alignment_and_Mutual_Masking_for_3D-Language_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Context-Aware_Alignment_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Context-Aware_Alignment_and_Mutual_Masking_for_3D-Language_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Context-Aware_Alignment_and_Mutual_Masking_for_3D-Language_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
MDL-NAS: A Joint Multi-Domain Learning Framework for Vision Transformer | Shiguang Wang, Tao Xie, Jian Cheng, Xingcheng Zhang, Haijun Liu | In this work, we introduce MDL-NAS, a unified framework that integrates multiple vision tasks into a manageable supernet and optimizes these tasks collectively under diverse dataset domains. MDL-NAS is storage-efficient since multiple models with a majority of shared parameters can be deposited into a single one. Technically, MDL-NAS constructs a coarse-to-fine search space, where the coarse search space offers various optimal architectures for different tasks while the fine search space provides fine-grained parameter sharing to tackle the inherent obstacles of multi-domain learning. In the fine search space, we suggest two parameter sharing policies, i.e., sequential sharing policy and mask sharing policy. Compared with previous works, such two sharing policies allow for the partial sharing and non-sharing of parameters at each layer of the network, hence attaining real fine-grained parameter sharing. Finally, we present a joint-subnet search algorithm that finds the optimal architecture and sharing parameters for each task within total resource constraints, challenging the traditional practice that downstream vision tasks are typically equipped with backbone networks designed for image classification. Experimentally, we demonstrate that MDL-NAS families fitted with non-hierarchical or hierarchical transformers deliver competitive performance for all tasks compared with state-of-the-art methods while maintaining efficient storage deployment and computation. We also demonstrate that MDL-NAS allows incremental learning and evades catastrophic forgetting when generalizing to a new task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MDL-NAS_A_Joint_Multi-Domain_Learning_Framework_for_Vision_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_MDL-NAS_A_Joint_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MDL-NAS_A_Joint_Multi-Domain_Learning_Framework_for_Vision_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MDL-NAS_A_Joint_Multi-Domain_Learning_Framework_for_Vision_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Dual Alignment Unsupervised Domain Adaptation for Video-Text Retrieval | Xiaoshuai Hao, Wanqian Zhang, Dayan Wu, Fei Zhu, Bo Li | Video-text retrieval is an emerging stream in both computer vision and natural language processing communities, which aims to find relevant videos given text queries. In this paper, we study the notoriously challenging task, i.e., Unsupervised Domain Adaptation Video-text Retrieval (UDAVR), wherein training and testing data come from different distributions. Previous works merely alleviate the domain shift, which however overlook the pairwise misalignment issue in target domain, i.e., there exist no semantic relationships between target videos and texts. To tackle this, we propose a novel method named Dual Alignment Domain Adaptation (DADA). Specifically, we first introduce the cross-modal semantic embedding to generate discriminative source features in a joint embedding space. Besides, we utilize the video and text domain adaptations to smoothly balance the minimization of the domain shifts. To tackle the pairwise misalignment in target domain, we introduce the Dual Alignment Consistency (DAC) to fully exploit the semantic information of both modalities in target domain. The proposed DAC adaptively aligns the video-text pairs which are more likely to be relevant in target domain, enabling that positive pairs are increasing progressively and the noisy ones will potentially be aligned in the later stages. To that end, our method can generate more truly aligned target pairs and ensure the discriminality of target features.Compared with the state-of-the-art methods, DADA achieves 20.18% and 18.61% relative improvements on R@1 under the setting of TGIF->MSRVTT and TGIF->MSVD respectively, demonstrating the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hao_Dual_Alignment_Unsupervised_Domain_Adaptation_for_Video-Text_Retrieval_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hao_Dual_Alignment_Unsupervised_Domain_Adaptation_for_Video-Text_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hao_Dual_Alignment_Unsupervised_Domain_Adaptation_for_Video-Text_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories | Samarth Sinha, Roman Shapovalov, Jeremy Reizenstein, Ignacio Rocco, Natalia Neverova, Andrea Vedaldi, David Novotny | Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on sparse rigid object reconstruction successfully learned such priors from large datasets such as CO3D. In this paper, we extend this approach to dynamic objects. We use cats and dogs as a representative example and introduce Common Pets in 3D (CoP3D), a collection of crowd-sourced videos showing around 4,200 distinct pets. CoP3D is one of the first large-scale datasets for benchmarking non-rigid 3D reconstruction "in the wild". We also propose Tracker-NeRF, a method for learning 4D reconstruction from our dataset. At test time, given a small number of video frames of an unseen sequence, Tracker-NeRF predicts the trajectories and dynamics of the 3D points and generates new views, interpolating viewpoint and time. Results on CoP3D reveal significantly better non-rigid new-view synthesis performance than existing baselines. The data is available on the project webpage: https://cop3d.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sinha_Common_Pets_in_3D_Dynamic_New-View_Synthesis_of_Real-Life_Deformable_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sinha_Common_Pets_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.03889 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sinha_Common_Pets_in_3D_Dynamic_New-View_Synthesis_of_Real-Life_Deformable_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sinha_Common_Pets_in_3D_Dynamic_New-View_Synthesis_of_Real-Life_Deformable_CVPR_2023_paper.html | CVPR 2023 | null |
Generalized Decoding for Pixel, Image, and Language | Xueyan Zou, Zi-Yi Dou, Jianwei Yang, Zhe Gan, Linjie Li, Chunyuan Li, Xiyang Dai, Harkirat Behl, Jianfeng Wang, Lu Yuan, Nanyun Peng, Lijuan Wang, Yong Jae Lee, Jianfeng Gao | We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decoder takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Further, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level visual-semantic understanding space, without any pseudo-labeling. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets; (2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks; and (3) flexibility for efficient finetuning and novel task composition. Code, demo, video and visualization are available at: https://x-decoder-vl.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zou_Generalized_Decoding_for_Pixel_Image_and_Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zou_Generalized_Decoding_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.11270 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zou_Generalized_Decoding_for_Pixel_Image_and_Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zou_Generalized_Decoding_for_Pixel_Image_and_Language_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Unified Scene Text Spotting Based on Sequence Generation | Taeho Kil, Seonghyeon Kim, Sukmin Seo, Yoonsik Kim, Daehee Kim | Sequence generation models have recently made significant progress in unifying various vision tasks. Although some auto-regressive models have demonstrated promising results in end-to-end text spotting, they use specific detection formats while ignoring various text shapes and are limited in the maximum number of text instances that can be detected. To overcome these limitations, we propose a UNIfied scene Text Spotter, called UNITS. Our model unifies various detection formats, including quadrilaterals and polygons, allowing it to detect text in arbitrary shapes. Additionally, we apply starting-point prompting to enable the model to extract texts from an arbitrary starting point, thereby extracting more texts beyond the number of instances it was trained on. Experimental results demonstrate that our method achieves competitive performance compared to state-of-the-art methods. Further analysis shows that UNITS can extract a larger number of texts than it was trained on. We provide the code for our method at https://github.com/clovaai/units. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kil_Towards_Unified_Scene_Text_Spotting_Based_on_Sequence_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kil_Towards_Unified_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03435 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kil_Towards_Unified_Scene_Text_Spotting_Based_on_Sequence_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kil_Towards_Unified_Scene_Text_Spotting_Based_on_Sequence_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Normal-Guided Garment UV Prediction for Human Re-Texturing | Yasamin Jafarian, Tuanfeng Y. Wang, Duygu Ceylan, Jimei Yang, Nathan Carr, Yi Zhou, Hyun Soo Park | Clothes undergo complex geometric deformations, which lead to appearance changes. To edit human videos in a physically plausible way, a texture map must take into account not only the garment transformation induced by the body movements and clothes fitting, but also its 3D fine-grained surface geometry. This poses, however, a new challenge of 3D reconstruction of dynamic clothes from an image or a video. In this paper, we show that it is possible to edit dressed human images and videos without 3D reconstruction. We estimate a geometry aware texture map between the garment region in an image and the texture space, a.k.a, UV map. Our UV map is designed to preserve isometry with respect to the underlying 3D surface by making use of the 3D surface normals predicted from the image. Our approach captures the underlying geometry of the garment in a self-supervised way, requiring no ground truth annotation of UV maps and can be readily extended to predict temporally coherent UV maps. We demonstrate that our method outperforms the state-of-the-art human UV map estimation approaches on both real and synthetic data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jafarian_Normal-Guided_Garment_UV_Prediction_for_Human_Re-Texturing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jafarian_Normal-Guided_Garment_UV_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.06504 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jafarian_Normal-Guided_Garment_UV_Prediction_for_Human_Re-Texturing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jafarian_Normal-Guided_Garment_UV_Prediction_for_Human_Re-Texturing_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Compact Representations for LiDAR Completion and Generation | Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, Raquel Urtasun | LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To address these issues, we present UltraLiDAR, a data-driven framework for scene-level LiDAR completion, LiDAR generation, and LiDAR manipulation. The crux of UltraLiDAR is a compact, discrete representation that encodes the point cloud's geometric structure, is robust to noise, and is easy to manipulate. We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost. Furthermore, by learning a prior over the discrete codebook, we can generate diverse, realistic LiDAR point clouds for self-driving. We evaluate the effectiveness of UltraLiDAR on sparse-to-dense LiDAR completion and LiDAR generation. Experiments show that densifying real-world point clouds with our approach can significantly improve the performance of downstream perception systems. Compared to prior art on LiDAR generation, our approach generates much more realistic point clouds. According to A/B test, over 98.5% of the time human participants prefer our results over those of previous methods. Please refer to project page https://waabi.ai/research/ultralidar/ for more information. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiong_Learning_Compact_Representations_for_LiDAR_Completion_and_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiong_Learning_Compact_Representations_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Learning_Compact_Representations_for_LiDAR_Completion_and_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Learning_Compact_Representations_for_LiDAR_Completion_and_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Computational Flash Photography Through Intrinsics | Sepideh Sarajian Maralan, Chris Careaga, Yagiz Aksoy | Flash is an essential tool as it often serves as the sole controllable light source in everyday photography. However, the use of flash is a binary decision at the time a photograph is captured with limited control over its characteristics such as strength or color. In this work, we study the computational control of the flash light in photographs taken with or without flash. We present a physically motivated intrinsic formulation for flash photograph formation and develop flash decomposition and generation methods for flash and no-flash photographs, respectively. We demonstrate that our intrinsic formulation outperforms alternatives in the literature and allows us to computationally control flash in in-the-wild images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Maralan_Computational_Flash_Photography_Through_Intrinsics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Maralan_Computational_Flash_Photography_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Maralan_Computational_Flash_Photography_Through_Intrinsics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Maralan_Computational_Flash_Photography_Through_Intrinsics_CVPR_2023_paper.html | CVPR 2023 | null |
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning With Hyperspherical Embeddings | Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer | Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where a few points (hubs) occur frequently in multiple nearest neighbour lists of other points. Hubness negatively impacts distance-based classification when hubs from one class appear often among the nearest neighbors of points from another class, degrading the classifier's performance. To address the hubness problem in FSL, we first prove that hubness can be eliminated by distributing representations uniformly on the hypersphere. We then propose two new approaches to embed representations on the hypersphere, which we prove optimize a tradeoff between uniformity and local similarity preservation -- reducing hubness while retaining class structure. Our experiments show that the proposed methods reduce hubness, and significantly improves transductive FSL accuracy for a wide range of classifiers. | https://openaccess.thecvf.com/content/CVPR2023/papers/Trosten_Hubs_and_Hyperspheres_Reducing_Hubness_and_Improving_Transductive_Few-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Trosten_Hubs_and_Hyperspheres_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Trosten_Hubs_and_Hyperspheres_Reducing_Hubness_and_Improving_Transductive_Few-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Trosten_Hubs_and_Hyperspheres_Reducing_Hubness_and_Improving_Transductive_Few-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering | Nan Zhang, Zhiyi Pan, Thomas H. Li, Wei Gao, Ge Li | Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies. However, compared to self-attention mechanism, we find graph convolutions show a stronger ability in capturing local geometry information with less computational cost. In this paper, we employ a hybrid architecture design to construct our Graph Convolution Network with Attentive Filtering (AF-GCN), which takes advantage of both graph convolution and self-attention mechanism. We adopt graph convolutions to aggregate local features in the shallow encoder stages, while in the deeper stages, we propose a self-attention-like module named Graph Attentive Filter (GAF) to better model long-range contexts from distant neighbors. Besides, to further improve graph representation for point cloud segmentation, we employ a Spatial Feature Projection (SFP) module for graph convolutions which helps to handle spatial variations of unstructured point clouds. Finally, a graph-shared down-sampling and up-sampling strategy is introduced to make full use of the graph structures in point cloud processing. We conduct extensive experiments on multiple datasets including S3DIS, ScanNetV2, Toronto-3D, and ShapeNetPart. Experimental results show our AF-GCN obtains competitive performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Improving_Graph_Representation_for_Point_Cloud_Segmentation_via_Attentive_Filtering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Improving_Graph_Representation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Improving_Graph_Representation_for_Point_Cloud_Segmentation_via_Attentive_Filtering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Improving_Graph_Representation_for_Point_Cloud_Segmentation_via_Attentive_Filtering_CVPR_2023_paper.html | CVPR 2023 | null |
SpaText: Spatio-Textual Representation for Controllable Image Generation | Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischinski, Ohad Fried, Xi Yin | Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/objects or their layout in a fine-grained fashion. Previous attempts to provide such controls were hindered by their reliance on a fixed set of labels. To this end, we present SpaText --- a new method for text-to-image generation using open-vocabulary scene control. In addition to a global text prompt that describes the entire scene, the user provides a segmentation map where each region of interest is annotated by a free-form natural language description. Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based. In addition, we show how to extend the classifier-free guidance method in diffusion models to the multi-conditional case and present an alternative accelerated inference algorithm. Finally, we offer several automatic evaluation metrics and use them, in addition to FID scores and a user study, to evaluate our method and show that it achieves state-of-the-art results on image generation with free-form textual scene control. | https://openaccess.thecvf.com/content/CVPR2023/papers/Avrahami_SpaText_Spatio-Textual_Representation_for_Controllable_Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Avrahami_SpaText_Spatio-Textual_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14305 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Avrahami_SpaText_Spatio-Textual_Representation_for_Controllable_Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Avrahami_SpaText_Spatio-Textual_Representation_for_Controllable_Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
The ObjectFolder Benchmark: Multisensory Learning With Neural and Real Objects | Ruohan Gao, Yiming Dou, Hao Li, Tanmay Agarwal, Jeannette Bohg, Yunzhu Li, Li Fei-Fei, Jiajun Wu | We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulation with sight, sound, and touch. We also introduce the ObjectFolder Real dataset, including the multisensory measurements for 100 real-world household objects, building upon a newly designed pipeline for collecting the 3D meshes, videos, impact sounds, and tactile readings of real-world objects. For each task in the ObjectFolder Benchmark, we conduct systematic benchmarking on both the 1,000 multisensory neural objects from ObjectFolder, and the real multisensory data from ObjectFolder Real. Our results demonstrate the importance of multisensory perception and reveal the respective roles of vision, audio, and touch for different object-centric learning tasks. By publicly releasing our dataset and benchmark suite, we hope to catalyze and enable new research in multisensory object-centric learning in computer vision, robotics, and beyond. Project page: https://objectfolder.stanford.edu | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_The_ObjectFolder_Benchmark_Multisensory_Learning_With_Neural_and_Real_Objects_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_The_ObjectFolder_Benchmark_Multisensory_Learning_With_Neural_and_Real_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_The_ObjectFolder_Benchmark_Multisensory_Learning_With_Neural_and_Real_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients | Fatih Ilhan, Gong Su, Ling Liu | Federated learning (FL) is an attractive distributed learning paradigm supporting real-time continuous learning and client privacy by default. In most FL approaches, all edge clients are assumed to have sufficient computation capabilities to participate in the learning of a deep neural network (DNN) model. However, in real-life applications, some clients may have severely limited resources and can only train a much smaller local model. This paper presents ScaleFL, a novel FL approach with two distinctive mechanisms to handle resource heterogeneity and provide an equitable FL framework for all clients. First, ScaleFL adaptively scales down the DNN model along width and depth dimensions by leveraging early exits to find the best-fit models for resource-aware local training on distributed clients. In this way, ScaleFL provides an efficient balance of preserving basic and complex features in local model splits with various sizes for joint training while enabling fast inference for model deployment. Second, ScaleFL utilizes self-distillation among exit predictions during training to improve aggregation through knowledge transfer among subnetworks. We conduct extensive experiments on benchmark CV (CIFAR-10/100, ImageNet) and NLP datasets (SST-2, AgNews). We demonstrate that ScaleFL outperforms existing representative heterogeneous FL approaches in terms of global/local model performance and provides inference efficiency, with up to 2x latency and 4x model size reduction with negligible performance drop below 2%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ilhan_ScaleFL_Resource-Adaptive_Federated_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.html | CVPR 2023 | null |
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Klingner_X3KD_Knowledge_Distillation_Across_Modalities_Tasks_and_Stages_for_Multi-Camera_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Klingner_X3KD_Knowledge_Distillation_Across_Modalities_Tasks_and_Stages_for_Multi-Camera_CVPR_2023_paper.html | CVPR 2023 | null |
PCT-Net: Full Resolution Image Harmonization Using Pixel-Wise Color Transformations | Julian Jorge Andrade Guerreiro, Mitsuru Nakazawa, Björn Stenger | In this paper, we present PCT-Net, a simple and general image harmonization method that can be easily applied to images at full resolution. The key idea is to learn a parameter network that uses downsampled input images to predict the parameters for pixel-wise color transforms (PCTs) which are applied to each pixel in the full-resolution image. We show that affine color transforms are both efficient and effective, resulting in state-of-the-art harmonization results. Moreover, we explore both CNNs and Transformers as the parameter network and show that Transformers lead to better results. We evaluate the proposed method on the public full-resolution iHarmony4 dataset, which is comprised of four datasets, and show a reduction of the foreground MSE (fMSE) and MSE values by more than 20% and an increase of the PSNR value by 1.4dB while keeping the architecture light-weight. In a user study with 20 people, we show that the method achieves a higher B-T score than two other recent methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guerreiro_PCT-Net_Full_Resolution_Image_Harmonization_Using_Pixel-Wise_Color_Transformations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guerreiro_PCT-Net_Full_Resolution_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guerreiro_PCT-Net_Full_Resolution_Image_Harmonization_Using_Pixel-Wise_Color_Transformations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guerreiro_PCT-Net_Full_Resolution_Image_Harmonization_Using_Pixel-Wise_Color_Transformations_CVPR_2023_paper.html | CVPR 2023 | null |
Architecture, Dataset and Model-Scale Agnostic Data-Free Meta-Learning | Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao | The goal of data-free meta-learning is to learn useful prior knowledge from a collection of pre-trained models without accessing their training data. However, existing works only solve the problem in parameter space, which (i) ignore the fruitful data knowledge contained in the pre-trained models; (ii) can not scale to large-scale pre-trained models; (iii) can only meta-learn pre-trained models with the same network architecture. To address those issues, we propose a unified framework, dubbed PURER, which contains: (1) ePisode cUrriculum inveRsion (ECI) during data-free meta training; and (2) invErsion calibRation following inner loop (ICFIL) during meta testing. During meta training, we propose ECI to perform pseudo episode training for learning to adapt fast to new unseen tasks. Specifically, we progressively synthesize a sequence of pseudo episodes by distilling the training data from each pre-trained model. The ECI adaptively increases the difficulty level of pseudo episodes according to the real-time feedback of the meta model. We formulate the optimization process of meta training with ECI as an adversarial form in an end-to-end manner. During meta testing, we further propose a simple plug-and-play supplement--ICFIL--only used during meta testing to narrow the gap between meta training and meta testing task distribution. Extensive experiments in various real-world scenarios show the superior performance of ours. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Architecture_Dataset_and_Model-Scale_Agnostic_Data-Free_Meta-Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Architecture_Dataset_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11183 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Architecture_Dataset_and_Model-Scale_Agnostic_Data-Free_Meta-Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Architecture_Dataset_and_Model-Scale_Agnostic_Data-Free_Meta-Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Egocentric Video Task Translation | Zihui Xue, Yale Song, Kristen Grauman, Lorenzo Torresani | Different video understanding tasks are typically treated in isolation, and even with distinct types of curated data (e.g., classifying sports in one dataset, tracking animals in another). However, in wearable cameras, the immersive egocentric perspective of a person engaging with the world around them presents an interconnected web of video understanding tasks---hand-object manipulations, navigation in the space, or human-human interactions---that unfold continuously, driven by the person's goals. We argue that this calls for a much more unified approach. We propose EgoTask Translation (EgoT2), which takes a collection of models optimized on separate tasks and learns to translate their outputs for improved performance on any or all of them at once. Unlike traditional transfer or multi-task learning, EgoT2's "flipped design" entails separate task-specific backbones and a task translator shared across all tasks, which captures synergies between even heterogeneous tasks and mitigates task competition. Demonstrating our model on a wide array of video tasks from Ego4D, we show its advantages over existing transfer paradigms and achieve top-ranked results on four of the Ego4D 2022 benchmark challenges. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xue_Egocentric_Video_Task_Translation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xue_Egocentric_Video_Task_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_Egocentric_Video_Task_Translation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_Egocentric_Video_Task_Translation_CVPR_2023_paper.html | CVPR 2023 | null |
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments | Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, Takeshi Ohashi | Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yoshimura_Rawgment_Noise-Accounted_RAW_Augmentation_Enables_Recognition_in_a_Wide_Variety_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yoshimura_Rawgment_Noise-Accounted_RAW_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.16046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshimura_Rawgment_Noise-Accounted_RAW_Augmentation_Enables_Recognition_in_a_Wide_Variety_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshimura_Rawgment_Noise-Accounted_RAW_Augmentation_Enables_Recognition_in_a_Wide_Variety_CVPR_2023_paper.html | CVPR 2023 | null |
Reliable and Interpretable Personalized Federated Learning | Zixuan Qin, Liu Yang, Qilong Wang, Yahong Han, Qinghua Hu | Federated learning can coordinate multiple users to participate in data training while ensuring data privacy. The collaboration of multiple agents allows for a natural connection between federated learning and collective intelligence. When there are large differences in data distribution among clients, it is crucial for federated learning to design a reliable client selection strategy and an interpretable client communication framework to better utilize group knowledge. Herein, a reliable personalized federated learning approach, termed RIPFL, is proposed and fully interpreted from the perspective of social learning. RIPFL reliably selects and divides the clients involved in training such that each client can use different amounts of social information and more effectively communicate with other clients. Simultaneously, the method effectively integrates personal information with the social information generated by the global model from the perspective of Bayesian decision rules and evidence theory, enabling individuals to grow better with the help of collective wisdom. An interpretable federated learning mind is well scalable, and the experimental results indicate that the proposed method has superior robustness and accuracy than other state-of-the-art federated learning algorithms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting | Wei Lin, Antoni B. Chan | The accuracy of crowd counting in images has improved greatly in recent years due to the development of deep neural networks for predicting crowd density maps. However, most methods do not further explore the ability to localize people in the density map, with those few works adopting simple methods, like finding the local peaks in the density map. In this paper, we propose the optimal transport minimization (OT-M) algorithm for crowd localization with density maps. The objective of OT-M is to find a target point map that has the minimal Sinkhorn distance with the input density map, and we propose an iterative algorithm to compute the solution. We then apply OT-M to generate hard pseudo-labels (point maps) for semi-supervised counting, rather than the soft pseudo-labels (density maps) used in previous methods. Our hard pseudo-labels provide stronger supervision, and also enable the use of recent density-to-point loss functions for training. We also propose a confidence weighting strategy to give higher weight to the more reliable unlabeled data. Extensive experiments show that our methods achieve outstanding performance on both crowd localization and semi-supervised counting. Code is available at https://github.com/Elin24/OT-M. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Optimal_Transport_Minimization_Crowd_Localization_on_Density_Maps_for_Semi-Supervised_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Optimal_Transport_Minimization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Optimal_Transport_Minimization_Crowd_Localization_on_Density_Maps_for_Semi-Supervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Optimal_Transport_Minimization_Crowd_Localization_on_Density_Maps_for_Semi-Supervised_CVPR_2023_paper.html | CVPR 2023 | null |
AdamsFormer for Spatial Action Localization in the Future | Hyung-gun Chi, Kwonjoon Lee, Nakul Agarwal, Yi Xu, Karthik Ramani, Chiho Choi | Predicting future action locations is vital for applications like human-robot collaboration. While some computer vision tasks have made progress in predicting human actions, accurately localizing these actions in future frames remains an area with room for improvement. We introduce a new task called spatial action localization in the future (SALF), which aims to predict action locations in both observed and future frames. SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately. To address SALF, we use the concept of NeuralODE, which models the latent dynamics of sequential data by solving ordinary differential equations (ODE) with neural networks. We propose a novel architecture, AdamsFormer, which extends observed frame features to future time horizons by modeling continuous temporal dynamics through ODE solving. Specifically, we employ the Adams method, a multi-step approach that efficiently uses information from previous steps without discarding it. Our extensive experiments on UCF101-24 and JHMDB-21 datasets demonstrate that our proposed model outperforms existing long-range temporal modeling methods by a significant margin in terms of frame-mAP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chi_AdamsFormer_for_Spatial_Action_Localization_in_the_Future_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chi_AdamsFormer_for_Spatial_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chi_AdamsFormer_for_Spatial_Action_Localization_in_the_Future_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chi_AdamsFormer_for_Spatial_Action_Localization_in_the_Future_CVPR_2023_paper.html | CVPR 2023 | null |
Leveraging per Image-Token Consistency for Vision-Language Pre-Training | Yunhao Gou, Tom Ko, Hansi Yang, James Kwok, Yu Zhang, Mingxuan Wang | Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://github.com/gyhdog99/epic | https://openaccess.thecvf.com/content/CVPR2023/papers/Gou_Leveraging_per_Image-Token_Consistency_for_Vision-Language_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gou_Leveraging_per_Image-Token_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15398 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gou_Leveraging_per_Image-Token_Consistency_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gou_Leveraging_per_Image-Token_Consistency_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
BITE: Beyond Priors for Improved Three-D Dog Pose Estimation | Nadine Rüegg, Shashank Tripathi, Konrad Schindler, Michael J. Black, Silvia Zuffi | We address the problem of inferring the 3D shape and pose of dogs from images. Given the lack of 3D training data, this problem is challenging, and the best methods lag behind those designed to estimate human shape and pose. To make progress, we attack the problem from multiple sides at once. First, we need a good 3D shape prior, like those available for humans. To that end, we learn a dog-specific 3D parametric model, called D-SMAL. Second, existing methods focus on dogs in standing poses because when they sit or lie down, their legs are self occluded and their bodies deform. Without access to a good pose prior or 3D data, we need an alternative approach. To that end, we exploit contact with the ground as a form of side information. We consider an existing large dataset of dog images and label any 3D contact of the dog with the ground. We exploit body-ground contact in estimating dog pose and find that it significantly improves results. Third, we develop a novel neural network architecture to infer and exploit this contact information. Fourth, to make progress, we have to be able to measure it. Current evaluation metrics are based on 2D features like keypoints and silhouettes, which do not directly correlate with 3D errors. To address this, we create a synthetic dataset containing rendered images of scanned 3D dogs. With these advances, our method recovers significantly better dog shape and pose than the state of the art, and we evaluate this improvement in 3D. Our code, model and test dataset are publicly available for research purposes at https://bite.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ruegg_BITE_Beyond_Priors_for_Improved_Three-D_Dog_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ruegg_BITE_Beyond_Priors_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ruegg_BITE_Beyond_Priors_for_Improved_Three-D_Dog_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ruegg_BITE_Beyond_Priors_for_Improved_Three-D_Dog_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution | Jiahao Chao, Zhou Zhou, Hongfan Gao, Jiali Gong, Zhengfeng Yang, Zhenbing Zeng, Lydia Dehbi | In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices. To address this issue, many lightweight super-resolution models have been proposed. However, these models still contain time-consuming components that increase inference latency, limiting their real-world applications on mobile devices. In this paper, we propose a novel model for singleimage super-resolution based on Equivalent Transformation and Dual Stream network construction (ETDS). ET method is proposed to transform time-consuming operators into time-friendly ones such as convolution and ReLU on mobile devices. Then, a dual stream network is designed to alleviate redundant parameters yielded from ET and enhance the feature extraction ability. Taking full advantage of the advance of ET and the dual stream network structure, we develop the efficient SR model ETDS for mobile devices. The experimental results demonstrate that our ETDS achieves superior inference speed and reconstruction quality compared to prior lightweight SR methods on mobile devices. The code is available at https://github.com/ECNUSR/ETDS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chao_Equivalent_Transformation_and_Dual_Stream_Network_Construction_for_Mobile_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chao_Equivalent_Transformation_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chao_Equivalent_Transformation_and_Dual_Stream_Network_Construction_for_Mobile_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chao_Equivalent_Transformation_and_Dual_Stream_Network_Construction_for_Mobile_Image_CVPR_2023_paper.html | CVPR 2023 | null |
UTM: A Unified Multiple Object Tracking Model With Identity-Aware Feature Enhancement | Sisi You, Hantao Yao, Bing-Kun Bao, Changsheng Xu | Recently, Multiple Object Tracking has achieved great success, which consists of object detection, feature embedding, and identity association. Existing methods apply the three-step or two-step paradigm to generate robust trajectories, where identity association is independent of other components. However, the independent identity association results in the identity-aware knowledge contained in the tracklet not be used to boost the detection and embedding modules. To overcome the limitations of existing methods, we introduce a novel Unified Tracking Model (UTM) to bridge those three components for generating a positive feedback loop with mutual benefits. The key insight of UTM is the Identity-Aware Feature Enhancement (IAFE), which is applied to bridge and benefit these three components by utilizing the identity-aware knowledge to boost detection and embedding. Formally, IAFE contains the Identity-Aware Boosting Attention (IABA) and the Identity-Aware Erasing Attention (IAEA), where IABA enhances the consistent regions between the current frame feature and identity-aware knowledge, and IAEA suppresses the distracted regions in the current frame feature. With better detections and embeddings, higher-quality tracklets can also be generated. Extensive experiments of public and private detections on three benchmarks demonstrate the robustness of UTM. | https://openaccess.thecvf.com/content/CVPR2023/papers/You_UTM_A_Unified_Multiple_Object_Tracking_Model_With_Identity-Aware_Feature_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/You_UTM_A_Unified_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/You_UTM_A_Unified_Multiple_Object_Tracking_Model_With_Identity-Aware_Feature_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/You_UTM_A_Unified_Multiple_Object_Tracking_Model_With_Identity-Aware_Feature_CVPR_2023_paper.html | CVPR 2023 | null |
On the Stability-Plasticity Dilemma of Class-Incremental Learning | Dongwan Kim, Bohyung Han | A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from previously seen classes, and plastic enough to learn concepts from new classes. While previous works demonstrate strong performance on class-incremental benchmarks, it is not clear whether their success comes from the models being stable, plastic, or a mixture of both. This paper aims to shed light on how effectively recent class-incremental learning algorithms address the stability-plasticity trade-off. We establish analytical tools that measure the stability and plasticity of feature representations, and employ such tools to investigate models trained with various algorithms on large-scale class-incremental benchmarks. Surprisingly, we find that the majority of class-incremental learning algorithms heavily favor stability over plasticity, to the extent that the feature extractor of a model trained on the initial set of classes is no less effective than that of the final incremental model. Our observations not only inspire two simple algorithms that highlight the importance of feature representation analysis, but also suggest that class-incremental learning approaches, in general, should strive for better feature representation learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_On_the_Stability-Plasticity_Dilemma_of_Class-Incremental_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_On_the_Stability-Plasticity_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01663 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_On_the_Stability-Plasticity_Dilemma_of_Class-Incremental_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_On_the_Stability-Plasticity_Dilemma_of_Class-Incremental_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation | Tianli Zhang, Mengqi Xue, Jiangtao Zhang, Haofei Zhang, Yu Wang, Lechao Cheng, Jie Song, Mingli Song | Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability. In this paper, we strive to fully utilize multi-model settings instead of well-designed modules to achieve a distillation effect with excellent generalization performance. Generally, model generalization can be reflected in the flatness of the loss landscape. Since averaging parameters of multiple models can find flatter minima, we are inspired to extend the process to the sampled convex combinations of multi-student models in OKD. Specifically, by linearly weighting students' parameters in each training batch, we construct a Hybrid-Weight Model(HWM) to represent the parameters surrounding involved students. The supervision loss of HWM can estimate the landscape's curvature of the whole region around students to measure the generalization explicitly. Hence we integrate HWM's loss into students' training and propose a novel OKD framework via parameter hybridization(OKDPH) to promote flatter minima and obtain robust solutions. Considering the redundancy of parameters could lead to the collapse of HWM, we further introduce a fusion operation to keep the high similarity of students. Compared to the state-of-the-art(SOTA) OKD methods and SOTA methods of seeking flat minima, our OKDPH achieves higher performance with fewer parameters, benefiting OKD with lightweight and robust characteristics. Our code is publicly available at https://github.com/tianlizhang/OKDPH. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Generalization_Matters_Loss_Minima_Flattening_via_Parameter_Hybridization_for_Efficient_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Generalization_Matters_Loss_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Generalization_Matters_Loss_Minima_Flattening_via_Parameter_Hybridization_for_Efficient_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Generalization_Matters_Loss_Minima_Flattening_via_Parameter_Hybridization_for_Efficient_CVPR_2023_paper.html | CVPR 2023 | null |
Gaussian Label Distribution Learning for Spherical Image Object Detection | Hang Xu, Xinyuan Liu, Qiang Zhao, Yike Ma, Chenggang Yan, Feng Dai | Spherical image object detection emerges in many applications from virtual reality to robotics and automatic driving, while many existing detectors use ln-norms loss for regression of spherical bounding boxes. There are two intrinsic flaws for ln-norms loss, i.e., independent optimization of parameters and inconsistency between metric (dominated by IoU) and loss. These problems are common in planar image detection but more significant in spherical image detection. Solution for these problems has been extensively discussed in planar image detection by using IoU loss and related variants. However, these solutions cannot be migrated to spherical image object detection due to the undifferentiable of the Spherical IoU (SphIoU). In this paper, we design a simple but effective regression loss based on Gaussian Label Distribution Learning (GLDL) for spherical image object detection. Besides, we observe that the scale of the object in a spherical image varies greatly. The huge differences among objects from different categories make the sample selection strategy based on SphIoU challenging. Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance. Extensive results on various two datasets with different baseline detectors show the effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Gaussian_Label_Distribution_Learning_for_Spherical_Image_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Gaussian_Label_Distribution_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Gaussian_Label_Distribution_Learning_for_Spherical_Image_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Gaussian_Label_Distribution_Learning_for_Spherical_Image_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
High-Resolution Image Reconstruction With Latent Diffusion Models From Human Brain Activity | Yu Takagi, Shinji Nishimoto | Reconstructing visual experiences from human brain activity offers a unique way to understand how the brain represents the world, and to interpret the connection between computer vision models and our visual system. While deep generative models have recently been employed for this task, reconstructing realistic images with high semantic fidelity is still a challenging problem. Here, we propose a new method based on a diffusion model (DM) to reconstruct images from human brain activity obtained via functional magnetic resonance imaging (fMRI). More specifically, we rely on a latent diffusion model (LDM) termed Stable Diffusion. This model reduces the computational cost of DMs, while preserving their high generative performance. We also characterize the inner mechanisms of the LDM by studying how its different components (such as the latent vector Z, conditioning inputs C, and different elements of the denoising U-Net) relate to distinct brain functions. We show that our proposed method can reconstruct high-resolution images with high fidelity in straightforward fashion, without the need for any additional training and fine-tuning of complex deep-learning models. We also provide a quantitative interpretation of different LDM components from a neuroscientific perspective. Overall, our study proposes a promising method for reconstructing images from human brain activity, and provides a new framework for understanding DMs. Please check out our webpage at https://sites.google.com/view/stablediffusion-withbrain/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Takagi_High-Resolution_Image_Reconstruction_With_Latent_Diffusion_Models_From_Human_Brain_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Takagi_High-Resolution_Image_Reconstruction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Takagi_High-Resolution_Image_Reconstruction_With_Latent_Diffusion_Models_From_Human_Brain_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Takagi_High-Resolution_Image_Reconstruction_With_Latent_Diffusion_Models_From_Human_Brain_CVPR_2023_paper.html | CVPR 2023 | null |
L-CoIns: Language-Based Colorization With Instance Awareness | Zheng Chang, Shuchen Weng, Peixuan Zhang, Yu Li, Si Li, Boxin Shi | Language-based colorization produces plausible colors consistent with the language description provided by the user. Recent studies introduce additional annotation to prevent color-object coupling and mismatch issues, but they still have difficulty in distinguishing instances corresponding to the same object words. In this paper, we propose a transformer-based framework to automatically aggregate similar image patches and achieve instance awareness without any additional knowledge. By applying our presented luminance augmentation and counter-color loss to break down the statistical correlation between luminance and color words, our model is driven to synthesize colors with better descriptive consistency. We further collect a dataset to provide distinctive visual characteristics and detailed language descriptions for multiple instances in the same image. Extensive experiments demonstrate our advantages of synthesizing visually pleasing and description-consistent results of instance-aware colorization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chang_L-CoIns_Language-Based_Colorization_With_Instance_Awareness_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chang_L-CoIns_Language-Based_Colorization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_L-CoIns_Language-Based_Colorization_With_Instance_Awareness_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_L-CoIns_Language-Based_Colorization_With_Instance_Awareness_CVPR_2023_paper.html | CVPR 2023 | null |
On the Effects of Self-Supervision and Contrastive Alignment in Deep Multi-View Clustering | Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael C. Kampffmeyer | Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially slowing the progress of the field. To address this, we present DeepMVC, a unified framework for deep MVC that includes many recent methods as instances. We leverage our framework to make key observations about the effect of self-supervision, and in particular, drawbacks of aligning representations with contrastive learning. Further, we prove that contrastive alignment can negatively influence cluster separability, and that this effect becomes worse when the number of views increases. Motivated by our findings, we develop several new DeepMVC instances with new forms of self-supervision. We conduct extensive experiments and find that (i) in line with our theoretical findings, contrastive alignments decreases performance on datasets with many views; (ii) all methods benefit from some form of self-supervision; and (iii) our new instances outperform previous methods on several datasets. Based on our results, we suggest several promising directions for future research. To enhance the openness of the field, we provide an open-source implementation of DeepMVC, including recent models and our new instances. Our implementation includes a consistent evaluation protocol, facilitating fair and accurate evaluation of methods and components. | https://openaccess.thecvf.com/content/CVPR2023/papers/Trosten_On_the_Effects_of_Self-Supervision_and_Contrastive_Alignment_in_Deep_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Trosten_On_the_Effects_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Trosten_On_the_Effects_of_Self-Supervision_and_Contrastive_Alignment_in_Deep_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Trosten_On_the_Effects_of_Self-Supervision_and_Contrastive_Alignment_in_Deep_CVPR_2023_paper.html | CVPR 2023 | null |
Activating More Pixels in Image Super-Resolution Transformer | Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong | Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB. Codes and models are available at https://github.com/XPixelGroup/HAT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Activating_More_Pixels_in_Image_Super-Resolution_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Activating_More_Pixels_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.04437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Activating_More_Pixels_in_Image_Super-Resolution_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Activating_More_Pixels_in_Image_Super-Resolution_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks | Xiaowei Chi, Jiaming Liu, Ming Lu, Rongyu Zhang, Zhaoqing Wang, Yandong Guo, Shanghang Zhang | Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm consisting of three essential components, i.e., camera feature extraction, BEV feature construction, and task heads. Among the three components, BEV feature construction is BEV-specific compared with 2D tasks. Existing methods aggregate the multi-view camera features to the flattened grid in order to construct the BEV feature. However, flattening the BEV space along the height dimension fails to emphasize the informative features of different heights. For example, the barrier is located at a low height while the truck is located at a high height. In this paper, we propose a novel method named BEV Slice Attention Network (BEV-SAN) for exploiting the intrinsic characteristics of different heights. Instead of flattening the BEV space, we first sample along the height dimension to build the global and local BEV slices. Then, the features of BEV slices are aggregated from the camera features and merged by the attention mechanism. Finally, we fuse the merged local and global BEV features by a transformer to generate the final feature map for task heads. The purpose of local BEV slices is to emphasize informative heights. In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices. Compared with uniform sampling, LiDAR-guided sampling can determine more informative heights. We conduct detailed experiments to demonstrate the effectiveness of BEV-SAN. Code will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chi_BEV-SAN_Accurate_BEV_3D_Object_Detection_via_Slice_Attention_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chi_BEV-SAN_Accurate_BEV_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chi_BEV-SAN_Accurate_BEV_3D_Object_Detection_via_Slice_Attention_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chi_BEV-SAN_Accurate_BEV_3D_Object_Detection_via_Slice_Attention_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection | Simin Chen, Hanlin Chen, Mirazul Haque, Cong Liu, Wei Yang | Recent advancements in deploying deep neural networks (DNNs) on resource-constrained devices have generated interest in input-adaptive dynamic neural networks (DyNNs). DyNNs offer more efficient inferences and enable the deployment of DNNs on devices with limited resources, such as mobile devices. However, we have discovered a new vulnerability in DyNNs that could potentially compromise their efficiency. Specifically, we investigate whether adversaries can manipulate DyNNs' computational costs to create a false sense of efficiency. To address this question, we propose EfficFrog, an adversarial attack that injects universal efficiency backdoors in DyNNs. To inject a backdoor trigger into DyNNs, EfficFrog poisons only a minimal percentage of the DyNNs' training data. During the inference phase, EfficFrog can slow down the backdoored DyNNs and abuse the computational resources of systems running DyNNs by adding the trigger to any input. To evaluate EfficFrog, we tested it on three DNN backbone architectures (based on VGG16, MobileNet, and ResNet56) using two popular datasets (CIFAR-10 and Tiny ImageNet). Our results demonstrate that EfficFrog reduces the efficiency of DyNNs on triggered input samples while keeping the efficiency of clean samples almost the same. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_The_Dark_Side_of_Dynamic_Routing_Neural_Networks_Towards_Efficiency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_The_Dark_Side_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_The_Dark_Side_of_Dynamic_Routing_Neural_Networks_Towards_Efficiency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_The_Dark_Side_of_Dynamic_Routing_Neural_Networks_Towards_Efficiency_CVPR_2023_paper.html | CVPR 2023 | null |
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution | Xuhai Chen, Jiangning Zhang, Chao Xu, Yabiao Wang, Chengjie Wang, Yong Liu | Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-variant due to object motion, out-of-focus, etc., resulting in severe performance drop of the advanced SR methods. To address this problem, we firstly introduce two new datasets with out-of-focus blur, i.e., NYUv2-BSR and Cityscapes-BSR, to support further researches of blind SR with space-variant blur. Based on the datasets, we design a novel Cross-MOdal fuSion network (CMOS) that estimate both blur and semantics simultaneously, which leads to improved SR results. It involves a feature Grouping Interactive Attention (GIA) module to make the two modals interact more effectively and avoid inconsistency. GIA can also be used for the interaction of other features because of the universality of its structure. Qualitative and quantitative experiments compared with state-of-the-art methods on above datasets and real-world images demonstrate the superiority of our method, e.g., obtaining PSNR/SSIM by +1.91/+0.0048 on NYUv2-BSR than MANet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Better_CMOS_Produces_Clearer_Images_Learning_Space-Variant_Blur_Estimation_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Better_CMOS_Produces_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03542 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Better_CMOS_Produces_Clearer_Images_Learning_Space-Variant_Blur_Estimation_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Better_CMOS_Produces_Clearer_Images_Learning_Space-Variant_Blur_Estimation_for_CVPR_2023_paper.html | CVPR 2023 | null |
MixTeacher: Mining Promising Labels With Mixed Scale Teacher for Semi-Supervised Object Detection | Liang Liu, Boshen Zhang, Jiangning Zhang, Wuhao Zhang, Zhenye Gan, Guanzhong Tian, Wenbing Zhu, Yabiao Wang, Chengjie Wang | Scale variation across object instances is one of the key challenges in object detection. Although modern detection models have achieved remarkable progress in dealing with the scale variation, it still brings trouble in the semi-supervised case. Most existing semi-supervised object detection methods rely on strict conditions to filter out high-quality pseudo labels from the network predictions. However, we observe that objects with extreme scale tend to have low confidence, which makes the positive supervision missing for these objects. In this paper, we delve into the scale variation problem, and propose a novel framework by introducing a mixed scale teacher to improve the pseudo labels generation and scale invariant learning. In addition, benefiting from the better predictions from mixed scale features, we propose to mine pseudo labels with the score promotion of predictions across scales. Extensive experiments on MS COCO and PASCAL VOC benchmarks under various semi-supervised settings demonstrate that our method achieves new state-of-the-art performance. The code and models will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_MixTeacher_Mining_Promising_Labels_With_Mixed_Scale_Teacher_for_Semi-Supervised_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_MixTeacher_Mining_Promising_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09061 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MixTeacher_Mining_Promising_Labels_With_Mixed_Scale_Teacher_for_Semi-Supervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MixTeacher_Mining_Promising_Labels_With_Mixed_Scale_Teacher_for_Semi-Supervised_CVPR_2023_paper.html | CVPR 2023 | null |
DARE-GRAM: Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices | Ismail Nejjar, Qin Wang, Olga Fink | Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepancy between source and target features. In this work, we present a different perspective for the DAR problem by analyzing the closed-form ordinary least square (OLS) solution to the linear regressor in the deep domain adaptation context. Rather than aligning the original feature embedding space, we propose to align the inverse Gram matrix of the features, which is motivated by its presence in the OLS solution and the Gram matrix's ability to capture the feature correlations. Specifically, we propose a simple yet effective DAR method which leverages the pseudo-inverse low-rank property to align the scale and angle in a selected subspace generated by the pseudo-inverse Gram matrix of the two domains. We evaluate our method on three domain adaptation regression benchmarks. Experimental results demonstrate that our method achieves state-of-the-art performance. Our code is available at https://github.com/ismailnejjar/DARE-GRAM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nejjar_DARE-GRAM_Unsupervised_Domain_Adaptation_Regression_by_Aligning_Inverse_Gram_Matrices_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nejjar_DARE-GRAM_Unsupervised_Domain_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13325 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nejjar_DARE-GRAM_Unsupervised_Domain_Adaptation_Regression_by_Aligning_Inverse_Gram_Matrices_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nejjar_DARE-GRAM_Unsupervised_Domain_Adaptation_Regression_by_Aligning_Inverse_Gram_Matrices_CVPR_2023_paper.html | CVPR 2023 | null |
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation | Yunhao Bai, Duowen Chen, Qingli Li, Wei Shen, Yan Wang | In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or training labeled and unlabeled data in an inconsistent manner. We propose a straightforward method for alleviating the problem -- copy-pasting labeled and unlabeled data bidirectionally, in a simple Mean Teacher architecture. The method encourages unlabeled data to learn comprehensive common semantics from the labeled data in both inward and outward directions. More importantly, the consistent learning procedure for labeled and unlabeled data can largely reduce the empirical distribution gap. In detail, we copy-paste a random crop from a labeled image (foreground) onto an unlabeled image (background) and an unlabeled image (foreground) onto a labeled image (background), respectively. The two mixed images are fed into a Student network. It is trained by the generated supervisory signal via bidirectional copy-pasting between the predictions of the unlabeled images from the Teacher and the label maps of the labeled images. We explore several design choices of how to copy-paste to make it more effective for minimizing empirical distribution gaps between labeled and unlabeled data. We reveal that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains (e.g., over 21% Dice improvement on ACDC dataset with 5% labeled data) compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_Bidirectional_Copy-Paste_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_Bidirectional_Copy-Paste_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.00673 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Bidirectional_Copy-Paste_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Bidirectional_Copy-Paste_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Discriminative Representations for Skeleton Based Action Recognition | Huanyu Zhou, Qingjie Liu, Yunhong Wang | Human action recognition aims at classifying the category of human action from a segment of a video. Recently, people have dived into designing GCN-based models to extract features from skeletons for performing this task, because skeleton representations are much more efficient and robust than other modalities such as RGB frames. However, when employing the skeleton data, some important clues like related items are also discarded. It results in some ambiguous actions that are hard to be distinguished and tend to be misclassified. To alleviate this problem, we propose an auxiliary feature refinement head (FR Head), which consists of spatial-temporal decoupling and contrastive feature refinement, to obtain discriminative representations of skeletons. Ambiguous samples are dynamically discovered and calibrated in the feature space. Furthermore, FR Head could be imposed on different stages of GCNs to build a multi-level refinement for stronger supervision. Extensive experiments are conducted on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets. Our proposed models obtain competitive results from state-of-the-art methods and can help to discriminate those ambiguous samples. Codes are available at https://github.com/zhysora/FR-Head. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Learning_Discriminative_Representations_for_Skeleton_Based_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Learning_Discriminative_Representations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03729 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Learning_Discriminative_Representations_for_Skeleton_Based_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Learning_Discriminative_Representations_for_Skeleton_Based_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis | Allan Zhou, Moo Jin Kim, Lirui Wang, Pete Florence, Chelsea Finn | Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert supervision to learn reactive closed-loop behaviors. In this work, we introduce SPARTN (Synthetic Perturbations for Augmenting Robot Trajectories via NeRF): a fully-offline data augmentation scheme for improving robot policies that use eye-in-hand cameras. Our approach leverages neural radiance fields (NeRFs) to synthetically inject corrective noise into visual demonstrations: using NeRFs to generate perturbed viewpoints while simultaneously calculating the corrective actions. This requires no additional expert supervision or environment interaction, and distills the geometric information in NeRFs into a real-time reactive RGB-only policy. In a simulated 6-DoF visual grasping benchmark, SPARTN improves offline success rates by 2.8x over imitation learning without the corrective augmentations and even outperforms some methods that use online supervision. It additionally closes the gap between RGB-only and RGB-D success rates, eliminating the previous need for depth sensors. In real-world 6-DoF robotic grasping experiments from limited human demonstrations, our method improves absolute success rates by 22.5% on average, including objects that are traditionally challenging for depth-based methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_NeRF_in_the_Palm_of_Your_Hand_Corrective_Augmentation_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_NeRF_in_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.08556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_NeRF_in_the_Palm_of_Your_Hand_Corrective_Augmentation_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_NeRF_in_the_Palm_of_Your_Hand_Corrective_Augmentation_for_CVPR_2023_paper.html | CVPR 2023 | null |
NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization | Shitao Tang, Sicong Tang, Andrea Tagliasacchi, Ping Tan, Yasutaka Furukawa | This paper presents an end-to-end neural mapping method for camera localization, dubbed NeuMap, encoding a whole scene into a grid of latent codes, with which a Transformer-based auto-decoder regresses 3D coordinates of query pixels. State-of-the-art feature matching methods require each scene to be stored as a 3D point cloud with per-point features, consuming several gigabytes of storage per scene. While compression is possible, performance drops significantly at high compression rates. Conversely, coordinate regression methods achieve high compression by storing scene information in a neural network but suffer from reduced robustness. NeuMap combines the advantages of both approaches by utilizing 1) learnable latent codes for efficient scene representation and 2) a scene-agnostic Transformer-based auto-decoder to infer coordinates for query pixels. This scene-agnostic network design learns robust matching priors from large-scale data and enables rapid optimization of codes for new scenes while keeping the network weights fixed. Extensive evaluations on five benchmarks show that NeuMap significantly outperforms other coordinate regression methods and achieves comparable performance to feature matching methods while requiring a much smaller scene representation size. For example, NeuMap achieves 39.1% accuracy in the Aachen night benchmark with only 6MB of data, whereas alternative methods require 100MB or several gigabytes and fail completely under high compression settings. The codes are available at https://github.com/Tangshitao/NeuMap. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_NeuMap_Neural_Coordinate_Mapping_by_Auto-Transdecoder_for_Camera_Localization_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.11177 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_NeuMap_Neural_Coordinate_Mapping_by_Auto-Transdecoder_for_Camera_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_NeuMap_Neural_Coordinate_Mapping_by_Auto-Transdecoder_for_Camera_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers | Zechuan Li, Hongshan Yu, Zhengeng Yang, Tongjia Chen, Naveed Akhtar | 3D object detection techniques commonly follow a pipeline that aggregates predicted object central point features to compute candidate points. However, these candidate points contain only positional information, largely ignoring the object-level shape information. This eventually leads to sub-optimal 3D object detection. In this work, we propose AShapeFormer, a semantics-guided object-level shape encoding module for 3D object detection. This is a plug-n-play module that leverages multi-head attention to encode object shape information. We also propose shape tokens and object-scene positional encoding to ensure that the shape information is fully exploited. Moreover, we introduce a semantic guidance sub-module to sample more foreground points and suppress the influence of background points for a better object shape perception. We demonstrate a straightforward enhancement of multiple existing methods with our AShapeFormer. Through extensive experiments on the popular SUN RGB-D and ScanNetV2 dataset, we show that our enhanced models are able to outperform the baselines by a considerable absolute margin of up to 8.1%. Code will be available at https://github.com/ZechuanLi/AShapeFormer | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_AShapeFormer_Semantics-Guided_Object-Level_Active_Shape_Encoding_for_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_AShapeFormer_Semantics-Guided_Object-Level_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_AShapeFormer_Semantics-Guided_Object-Level_Active_Shape_Encoding_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_AShapeFormer_Semantics-Guided_Object-Level_Active_Shape_Encoding_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
SeSDF: Self-Evolved Signed Distance Field for Implicit 3D Clothed Human Reconstruction | Yukang Cao, Kai Han, Kwan-Yee K. Wong | We address the problem of clothed human reconstruction from a single image or uncalibrated multi-view images. Existing methods struggle with reconstructing detailed geometry of a clothed human and often require a calibrated setting for multi-view reconstruction. We propose a flexible framework which, by leveraging the parametric SMPL-X model, can take an arbitrary number of input images to reconstruct a clothed human model under an uncalibrated setting. At the core of our framework is our novel self-evolved signed distance field (SeSDF) module which allows the framework to learn to deform the signed distance field (SDF) derived from the fitted SMPL-X model, such that detailed geometry reflecting the actual clothed human can be encoded for better reconstruction. Besides, we propose a simple method for self-calibration of multi-view images via the fitted SMPL-X parameters. This lifts the requirement of tedious manual calibration and largely increases the flexibility of our method. Further, we introduce an effective occlusion-aware feature fusion strategy to account for the most useful features to reconstruct the human model. We thoroughly evaluate our framework on public benchmarks, demonstrating significant superiority over the state-of-the-arts both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_SeSDF_Self-Evolved_Signed_Distance_Field_for_Implicit_3D_Clothed_Human_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_SeSDF_Self-Evolved_Signed_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_SeSDF_Self-Evolved_Signed_Distance_Field_for_Implicit_3D_Clothed_Human_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_SeSDF_Self-Evolved_Signed_Distance_Field_for_Implicit_3D_Clothed_Human_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Depth Estimation From Thermal Image | Ukcheol Shin, Jinsun Park, In So Kweon | Robust and accurate geometric understanding against adverse weather conditions is one top prioritized conditions to achieve a high-level autonomy of self-driving cars. However, autonomous driving algorithms relying on the visible spectrum band are easily impacted by weather and lighting conditions. A long-wave infrared camera, also known as a thermal imaging camera, is a potential rescue to achieve high-level robustness. However, the missing necessities are the well-established large-scale dataset and public benchmark results. To this end, in this paper, we first built a large-scale Multi-Spectral Stereo (MS^2) dataset, including stereo RGB, stereo NIR, stereo thermal, and stereo LiDAR data along with GNSS/IMU information. The collected dataset provides about 195K synchronized data pairs taken from city, residential, road, campus, and suburban areas in the morning, daytime, and nighttime under clear-sky, cloudy, and rainy conditions. Secondly, we conduct an exhaustive validation process of monocular and stereo depth estimation algorithms designed on visible spectrum bands to benchmark their performance in the thermal image domain. Lastly, we propose a unified depth network that effectively bridges monocular depth and stereo depth tasks from a conditional random field approach perspective. Our dataset and source code are available at https://github.com/UkcheolShin/MS2-MultiSpectralStereoDataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shin_Deep_Depth_Estimation_From_Thermal_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shin_Deep_Depth_Estimation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_Deep_Depth_Estimation_From_Thermal_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_Deep_Depth_Estimation_From_Thermal_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences Between Pretrained Generative Models | Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Weng-Keen Wong | Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for interpretable tools to audit trained networks, for example, to identify biases or ensure fairness. Existing GAN audit tools are restricted to coarse-grained, model-data comparisons based on summary statistics such as FID or recall. In this paper, we propose an alternative approach that compares a newly developed GAN against a prior baseline. To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "reference" GAN and a newly proposed "client" GAN, jointly identifies semantic attributes that are either common across both GANs, novel to the client GAN, or missing from the client GAN. This provides both users and model developers an intuitive assessment of similarity and differences between GANs. We introduce novel metrics to evaluate attribute-based GAN auditing approaches and use these metrics to demonstrate quantitatively that xGA outperforms baseline approaches. We also include qualitative results that illustrate the common, novel and missing attributes identified by xGA from GANs trained on a variety of image datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Olson_Cross-GAN_Auditing_Unsupervised_Identification_of_Attribute_Level_Similarities_and_Differences_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Olson_Cross-GAN_Auditing_Unsupervised_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10774 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Olson_Cross-GAN_Auditing_Unsupervised_Identification_of_Attribute_Level_Similarities_and_Differences_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Olson_Cross-GAN_Auditing_Unsupervised_Identification_of_Attribute_Level_Similarities_and_Differences_CVPR_2023_paper.html | CVPR 2023 | null |
Building Rearticulable Models for Arbitrary 3D Objects From 4D Point Clouds | Shaowei Liu, Saurabh Gupta, Shenlong Wang | We build rearticulable models for arbitrary everyday man-made objects containing an arbitrary number of parts that are connected together in arbitrary ways via 1-degree-of-freedom joints. Given point cloud videos of such everyday objects, our method identifies the distinct object parts, what parts are connected to what other parts, and the properties of the joints connecting each part pair. We do this by jointly optimizing the part segmentation, transformation, and kinematics using a novel energy minimization framework. Our inferred animatable models, enables retargeting to novel poses with sparse point correspondences guidance. We test our method on a new articulating robot dataset and the Sapiens dataset with common daily objects. Experiments show that our method outperforms two leading prior works on various metrics. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Building_Rearticulable_Models_for_Arbitrary_3D_Objects_From_4D_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Building_Rearticulable_Models_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Building_Rearticulable_Models_for_Arbitrary_3D_Objects_From_4D_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Building_Rearticulable_Models_for_Arbitrary_3D_Objects_From_4D_Point_CVPR_2023_paper.html | CVPR 2023 | null |
Backdoor Defense via Adaptively Splitting Poisoned Dataset | Kuofeng Gao, Yang Bai, Jindong Gu, Yong Yang, Shu-Tao Xia | Backdoor defenses have been studied to alleviate the threat of deep neural networks (DNNs) being backdoor attacked and thus maliciously altered. Since DNNs usually adopt some external training data from an untrusted third party, a robust backdoor defense strategy during the training stage is of importance. We argue that the core of training-time defense is to select poisoned samples and to handle them properly. In this work, we summarize the training-time defenses from a unified framework as splitting the poisoned dataset into two data pools. Under our framework, we propose an adaptively splitting dataset-based defense (ASD). Concretely, we apply loss-guided split and meta-learning-inspired split to dynamically update two data pools. With the split clean data pool and polluted data pool, ASD successfully defends against backdoor attacks during training. Extensive experiments on multiple benchmark datasets and DNN models against six state-of-the-art backdoor attacks demonstrate the superiority of our ASD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Backdoor_Defense_via_Adaptively_Splitting_Poisoned_Dataset_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_Backdoor_Defense_via_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Backdoor_Defense_via_Adaptively_Splitting_Poisoned_Dataset_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Backdoor_Defense_via_Adaptively_Splitting_Poisoned_Dataset_CVPR_2023_paper.html | CVPR 2023 | null |
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