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Progressively Optimized Local Radiance Fields for Robust View Synthesis | Andréas Meuleman, Yu-Lun Liu, Chen Gao, Jia-Bin Huang, Changil Kim, Min H. Kim, Johannes Kopf | We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the Tanks and Temples dataset and our collected outdoor dataset, Static Hikes, show that our approach compares favorably with the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Meuleman_Progressively_Optimized_Local_Radiance_Fields_for_Robust_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Meuleman_Progressively_Optimized_Local_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13791 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Meuleman_Progressively_Optimized_Local_Radiance_Fields_for_Robust_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Meuleman_Progressively_Optimized_Local_Radiance_Fields_for_Robust_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors | Gongjie Zhang, Zhipeng Luo, Zichen Tian, Jingyi Zhang, Xiaoqin Zhang, Shijian Lu | Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors. In this paper, we propose Iterative Multi-scale Feature Aggregation (IMFA) - a generic paradigm that enables efficient use of multi-scale features in Transformer-based object detectors. The core idea is to exploit sparse multi-scale features from just a few crucial locations, and it is achieved with two novel designs. First, IMFA rearranges the Transformer encoder-decoder pipeline so that the encoded features can be iteratively updated based on the detection predictions. Second, IMFA sparsely samples scale-adaptive features for refined detection from just a few keypoint locations under the guidance of prior detection predictions. As a result, the sampled multi-scale features are sparse yet still highly beneficial for object detection. Extensive experiments show that the proposed IMFA boosts the performance of multiple Transformer-based object detectors significantly yet with only slight computational overhead. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Towards_Efficient_Use_of_Multi-Scale_Features_in_Transformer-Based_Object_Detectors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Towards_Efficient_Use_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.11356 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Towards_Efficient_Use_of_Multi-Scale_Features_in_Transformer-Based_Object_Detectors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Towards_Efficient_Use_of_Multi-Scale_Features_in_Transformer-Based_Object_Detectors_CVPR_2023_paper.html | CVPR 2023 | null |
Delivering Arbitrary-Modal Semantic Segmentation | Jiaming Zhang, Ruiping Liu, Hao Shi, Kailun Yang, Simon Reiß, Kunyu Peng, Haodong Fu, Kaiwei Wang, Rainer Stiefelhagen | Multimodal fusion can make semantic segmentation more robust. However, fusing an arbitrary number of modalities remains underexplored. To delve into this problem, we create the DeLiVER arbitrary-modal segmentation benchmark, covering Depth, LiDAR, multiple Views, Events, and RGB. Aside from this, we provide this dataset in four severe weather conditions as well as five sensor failure cases to exploit modal complementarity and resolve partial outages. To facilitate this data, we present the arbitrary cross-modal segmentation model CMNeXt. It encompasses a Self-Query Hub (SQ-Hub) designed to extract effective information from any modality for subsequent fusion with the RGB representation and adds only negligible amounts of parameters ( 0.01M) per additional modality. On top, to efficiently and flexibly harvest discriminative cues from the auxiliary modalities, we introduce the simple Parallel Pooling Mixer (PPX). With extensive experiments on a total of six benchmarks, our CMNeXt achieves state-of-the-art performance, allowing to scale from 1 to 81 modalities on the DeLiVER, KITTI-360, MFNet, NYU Depth V2, UrbanLF, and MCubeS datasets. On the freshly collected DeLiVER, the quad-modal CMNeXt reaches up to 66.30% in mIoU with a +9.10% gain as compared to the mono-modal baseline. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Delivering_Arbitrary-Modal_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Delivering_Arbitrary-Modal_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01480 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Delivering_Arbitrary-Modal_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Delivering_Arbitrary-Modal_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
GeoMVSNet: Learning Multi-View Stereo With Geometry Perception | Zhe Zhang, Rui Peng, Yuxi Hu, Ronggang Wang | Recent cascade Multi-View Stereo (MVS) methods can efficiently estimate high-resolution depth maps through narrowing hypothesis ranges. However, previous methods ignored the vital geometric information embedded in coarse stages, leading to vulnerable cost matching and sub-optimal reconstruction results. In this paper, we propose a geometry awareness model, termed GeoMVSNet, to explicitly integrate geometric clues implied in coarse stages for delicate depth estimation. In particular, we design a two-branch geometry fusion network to extract geometric priors from coarse estimations to enhance structural feature extraction at finer stages. Besides, we embed the coarse probability volumes, which encode valuable depth distribution attributes, into the lightweight regularization network to further strengthen depth-wise geometry intuition. Meanwhile, we apply the frequency domain filtering to mitigate the negative impact of the high-frequency regions and adopt the curriculum learning strategy to progressively boost the geometry integration of the model. To intensify the full-scene geometry perception of our model, we present the depth distribution similarity loss based on the Gaussian-Mixture Model assumption. Extensive experiments on DTU and Tanks and Temples (T&T) datasets demonstrate that our GeoMVSNet achieves state-of-the-art results and ranks first on the T&T-Advanced set. Code is available at https://github.com/doubleZ0108/GeoMVSNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_GeoMVSNet_Learning_Multi-View_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.html | CVPR 2023 | null |
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-Targets in Semi-Supervised Object Detection | Xinjiang Wang, Xingyi Yang, Shilong Zhang, Yijiang Li, Litong Feng, Shijie Fang, Chengqi Lyu, Kai Chen, Wayne Zhang | In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo-targets undermine the training of an accurate detector. It injects noise into the student's training, leading to severe overfitting problems. Therefore, we propose a systematic solution, termed NAME, to reduce the inconsistency. First, adaptive anchor assignment (ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo-bounding boxes. Then we calibrate the subtask predictions by designing a 3D feature alignment module (FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of pseudo-bboxes, which stabilizes the number of ground truths at an early stage and remedies the unreliable supervision signal during training. NAME provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10% of annotated MS-COCO data, which surpasses previous baselines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.7 mAP. Our code is available at https://github.com/Adamdad/ConsistentTeacher. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Consistent-Teacher_Towards_Reducing_Inconsistent_Pseudo-Targets_in_Semi-Supervised_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Consistent-Teacher_Towards_Reducing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Consistent-Teacher_Towards_Reducing_Inconsistent_Pseudo-Targets_in_Semi-Supervised_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Consistent-Teacher_Towards_Reducing_Inconsistent_Pseudo-Targets_in_Semi-Supervised_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
OCTET: Object-Aware Counterfactual Explanations | Mehdi Zemni, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord | Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern. Among explanation methods, counterfactual explanations aim to find minimal and interpretable changes to the input image that would also change the output of the model to be explained. Such explanations point end-users at the main factors that impact the decision of the model. However, previous methods struggle to explain decision models trained on images with many objects, e.g., urban scenes, which are more difficult to work with but also arguably more critical to explain. In this work, we propose to tackle this issue with an object-centric framework for counterfactual explanation generation. Our method, inspired by recent generative modeling works, encodes the query image into a latent space that is structured in a way to ease object-level manipulations. Doing so, it provides the end-user with control over which search directions (e.g., spatial displacement of objects, style modification, etc.) are to be explored during the counterfactual generation. We conduct a set of experiments on counterfactual explanation benchmarks for driving scenes, and we show that our method can be adapted beyond classification, e.g., to explain semantic segmentation models. To complete our analysis, we design and run a user study that measures the usefulness of counterfactual explanations in understanding a decision model. Code is available at https://github.com/valeoai/OCTET. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zemni_OCTET_Object-Aware_Counterfactual_Explanations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zemni_OCTET_Object-Aware_Counterfactual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12380 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zemni_OCTET_Object-Aware_Counterfactual_Explanations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zemni_OCTET_Object-Aware_Counterfactual_Explanations_CVPR_2023_paper.html | CVPR 2023 | null |
TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation | Devavrat Tomar, Guillaume Vray, Behzad Bozorgtabar, Jean-Philippe Thiran | Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Test-time Self-Learning method with automatic Adversarial augmentation dubbed TeSLA for adapting a pre-trained source model to the unlabeled streaming test data. In contrast to conventional self-learning methods based on cross-entropy, we introduce a new test-time loss function through an implicitly tight connection with the mutual information and online knowledge distillation. Furthermore, we propose a learnable efficient adversarial augmentation module that further enhances online knowledge distillation by simulating high entropy augmented images. Our method achieves state-of-the-art classification and segmentation results on several benchmarks and types of domain shifts, particularly on challenging measurement shifts of medical images. TeSLA also benefits from several desirable properties compared to competing methods in terms of calibration, uncertainty metrics, insensitivity to model architectures, and source training strategies, all supported by extensive ablations. Our code and models are available at https://github.com/devavratTomar/TeSLA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tomar_TeSLA_Test-Time_Self-Learning_With_Automatic_Adversarial_Augmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tomar_TeSLA_Test-Time_Self-Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09870 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tomar_TeSLA_Test-Time_Self-Learning_With_Automatic_Adversarial_Augmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tomar_TeSLA_Test-Time_Self-Learning_With_Automatic_Adversarial_Augmentation_CVPR_2023_paper.html | CVPR 2023 | null |
DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos | Qi Zhao, M. Salman Asif, Zhan Ma | Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for 960 x 1920 videos. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_DNeRV_Modeling_Inherent_Dynamics_via_Difference_Neural_Representation_for_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_DNeRV_Modeling_Inherent_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06544 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_DNeRV_Modeling_Inherent_Dynamics_via_Difference_Neural_Representation_for_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_DNeRV_Modeling_Inherent_Dynamics_via_Difference_Neural_Representation_for_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
RefTeacher: A Strong Baseline for Semi-Supervised Referring Expression Comprehension | Jiamu Sun, Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Guannan Jiang, Zhiyu Wang, Rongrong Ji | Referring expression comprehension (REC) often requires a large number of instance-level annotations for fully supervised learning, which are laborious and expensive. In this paper, we present the first attempt of semi-supervised learning for REC and propose a strong baseline method called RefTeacher. Inspired by the recent progress in computer vision, RefTeacher adopts a teacher-student learning paradigm, where the teacher REC network predicts pseudo-labels for optimizing the student one. This paradigm allows REC models to exploit massive unlabeled data based on a small fraction of labeled. In particular, we also identify two key challenges in semi-supervised REC, namely, sparse supervision signals and worse pseudo-label noise. To address these issues, we equip RefTeacher with two novel designs called Attention-based Imitation Learning (AIL) and Adaptive Pseudo-label Weighting (APW). AIL can help the student network imitate the recognition behaviors of the teacher, thereby obtaining sufficient supervision signals. APW can help the model adaptively adjust the contributions of pseudo-labels with varying qualities, thus avoiding confirmation bias. To validate RefTeacher, we conduct extensive experiments on three REC benchmark datasets. Experimental results show that RefTeacher obtains obvious gains over the fully supervised methods. More importantly, using only 10% labeled data, our approach allows the model to achieve near 100% fully supervised performance, e.g., only -2.78% on RefCOCO. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_RefTeacher_A_Strong_Baseline_for_Semi-Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_RefTeacher_A_Strong_Baseline_for_Semi-Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_RefTeacher_A_Strong_Baseline_for_Semi-Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.html | CVPR 2023 | null |
Handwritten Text Generation From Visual Archetypes | Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara | Generating synthetic images of handwritten text in a writer-specific style is a challenging task, especially in the case of unseen styles and new words, and even more when these latter contain characters that are rarely encountered during training. While emulating a writer's style has been recently addressed by generative models, the generalization towards rare characters has been disregarded. In this work, we devise a Transformer-based model for Few-Shot styled handwritten text generation and focus on obtaining a robust and informative representation of both the text and the style. In particular, we propose a novel representation of the textual content as a sequence of dense vectors obtained from images of symbols written as standard GNU Unifont glyphs, which can be considered their visual archetypes. This strategy is more suitable for generating characters that, despite having been seen rarely during training, possibly share visual details with the frequently observed ones. As for the style, we obtain a robust representation of unseen writers' calligraphy by exploiting specific pre-training on a large synthetic dataset. Quantitative and qualitative results demonstrate the effectiveness of our proposal in generating words in unseen styles and with rare characters more faithfully than existing approaches relying on independent one-hot encodings of the characters. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pippi_Handwritten_Text_Generation_From_Visual_Archetypes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pippi_Handwritten_Text_Generation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15269 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pippi_Handwritten_Text_Generation_From_Visual_Archetypes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pippi_Handwritten_Text_Generation_From_Visual_Archetypes_CVPR_2023_paper.html | CVPR 2023 | null |
Unicode Analogies: An Anti-Objectivist Visual Reasoning Challenge | Steven Spratley, Krista A. Ehinger, Tim Miller | Analogical reasoning enables agents to extract relevant information from scenes, and efficiently navigate them in familiar ways. While progressive-matrix problems (PMPs) are becoming popular for the development and evaluation of analogical reasoning in computer vision, we argue that the dominant methodology in this area struggles to expose the lack of meaningful generalisation in solvers, and reinforces an objectivist stance on perception -- that objects can only be seen one way -- which we believe to be counter-productive. In this paper, we introduce the Unicode Analogies challenge, consisting of polysemic, character-based PMPs to benchmark fluid conceptualisation ability in vision systems. Writing systems have evolved characters at multiple levels of abstraction, from iconic through to symbolic representations, producing both visually interrelated yet exceptionally diverse images when compared to those exhibited by existing PMP datasets. Our framework has been designed to challenge models by presenting tasks much harder to complete without robust feature extraction, while remaining largely solvable by human participants. We therefore argue that Unicode Analogies elegantly captures and tests for a facet of human visual reasoning that is severely lacking in current-generation AI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Spratley_Unicode_Analogies_An_Anti-Objectivist_Visual_Reasoning_Challenge_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Spratley_Unicode_Analogies_An_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Spratley_Unicode_Analogies_An_Anti-Objectivist_Visual_Reasoning_Challenge_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Spratley_Unicode_Analogies_An_Anti-Objectivist_Visual_Reasoning_Challenge_CVPR_2023_paper.html | CVPR 2023 | null |
FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures | Weijie Chen, Xinyan Wang, Yuhang Wang | Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline meth- ods for building complete protein structures. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_FFF_Fragment-Guided_Flexible_Fitting_for_Building_Complete_Protein_Structures_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_FFF_Fragment-Guided_Flexible_Fitting_for_Building_Complete_Protein_Structures_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_FFF_Fragment-Guided_Flexible_Fitting_for_Building_Complete_Protein_Structures_CVPR_2023_paper.html | CVPR 2023 | null |
Polarized Color Image Denoising | Zhuoxiao Li, Haiyang Jiang, Mingdeng Cao, Yinqiang Zheng | Single-chip polarized color photography provides both visual textures and object surface information in one snapshot. However, the use of an additional directional polarizing filter array tends to lower photon count and SNR, when compared to conventional color imaging. As a result, such a bilayer structure usually leads to unpleasant noisy images and undermines performance of polarization analysis, especially in low-light conditions. It is a challenge for traditional image processing pipelines owing to the fact that the physical constraints exerted implicitly in the channels are excessively complicated. In this paper, we propose to tackle this issue through a noise modeling method for realistic data synthesis and a powerful network structure inspired by vision Transformer. A real-world polarized color image dataset of paired raw short-exposed noisy images and long-exposed reference images is captured for experimental evaluation, which has demonstrated the effectiveness of our approaches for data synthesis and polarized color image denoising. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Polarized_Color_Image_Denoising_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Polarized_Color_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Polarized_Color_Image_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Polarized_Color_Image_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation With Implicit Neural Representations | Rui Gong, Qin Wang, Martin Danelljan, Dengxin Dai, Luc Van Gool | Unsupervised domain adaptation (UDA) for semantic segmentation aims at improving the model performance on the unlabeled target domain by leveraging a labeled source domain. Existing approaches have achieved impressive progress by utilizing pseudo-labels on the unlabeled target-domain images. Yet the low-quality pseudo-labels, arising from the domain discrepancy, inevitably hinder the adaptation. This calls for effective and accurate approaches to estimating the reliability of the pseudo-labels, in order to rectify them. In this paper, we propose to estimate the rectification values of the predicted pseudo-labels with implicit neural representations. We view the rectification value as a signal defined over the continuous spatial domain. Taking an image coordinate and the nearby deep features as inputs, the rectification value at a given coordinate is predicted as an output. This allows us to achieve high-resolution and detailed rectification values estimation, important for accurate pseudo-label generation at mask boundaries in particular. The rectified pseudo-labels are then leveraged in our rectification-aware mixture model (RMM) to be learned end-to-end and help the adaptation. We demonstrate the effectiveness of our approach on different UDA benchmarks, including synthetic-to-real and day-to-night. Our approach achieves superior results compared to state-of-the-art. The implementation is available at https://github.com/ETHRuiGong/IR2F. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gong_Continuous_Pseudo-Label_Rectified_Domain_Adaptive_Semantic_Segmentation_With_Implicit_Neural_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gong_Continuous_Pseudo-Label_Rectified_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gong_Continuous_Pseudo-Label_Rectified_Domain_Adaptive_Semantic_Segmentation_With_Implicit_Neural_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gong_Continuous_Pseudo-Label_Rectified_Domain_Adaptive_Semantic_Segmentation_With_Implicit_Neural_CVPR_2023_paper.html | CVPR 2023 | null |
Hyperbolic Contrastive Learning for Visual Representations Beyond Objects | Songwei Ge, Shlok Mishra, Simon Kornblith, Chun-Liang Li, David Jacobs | Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this paper, we focus on learning representations of objects and scenes that preserve the structure among them. Motivated by the observation that visually similar objects are close in the representation space, we argue that the scenes and objects should instead follow a hierarchical structure based on their compositionality. To exploit such a structure, we propose a contrastive learning framework where a Euclidean loss is used to learn object representations and a hyperbolic loss is used to encourage representations of scenes to lie close to representations of their constituent objects in hyperbolic space. This novel hyperbolic objective encourages the scene-object hypernymy among the representations by optimizing the magnitude of their norms. We show that when pretraining on the COCO and OpenImages datasets, the hyperbolic loss improves the downstream performance of several baselines across multiple datasets and tasks, including image classification, object detection, and semantic segmentation. We also show that the properties of the learned representations allow us to solve various vision tasks that involve the interaction between scenes and objects in a zero-shot fashion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ge_Hyperbolic_Contrastive_Learning_for_Visual_Representations_Beyond_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ge_Hyperbolic_Contrastive_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00653 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ge_Hyperbolic_Contrastive_Learning_for_Visual_Representations_Beyond_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ge_Hyperbolic_Contrastive_Learning_for_Visual_Representations_Beyond_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Align Your Latents: High-Resolution Video Synthesis With Latent Diffusion Models | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Blattmann_Align_Your_Latents_High-Resolution_Video_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Blattmann_Align_Your_Latents_High-Resolution_Video_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training | Yifan Jiang, Peter Hedman, Ben Mildenhall, Dejia Xu, Jonathan T. Barron, Zhangyang Wang, Tianfan Xue | Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent effects, few efforts have been devoted to exploring its limits in a high-resolution setting. Specifically, existing NeRF-based methods face several limitations when reconstructing high-resolution real scenes, including a very large number of parameters, misaligned input data, and overly smooth details. In this work, we conduct the first pilot study on training NeRF with high-resolution data and propose the corresponding solutions: 1) marrying the multilayer perceptron (MLP) with convolutional layers which can encode more neighborhood information while reducing the total number of parameters; 2) a novel training strategy to address misalignment caused by moving objects or small camera calibration errors; and 3) a high-frequency aware loss. Our approach is nearly free without introducing obvious training/testing costs, while experiments on different datasets demonstrate that it can recover more high-frequency details compared with the current state-of-the-art NeRF models. Project page: https://yifanjiang19.github.io/alignerf. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_AligNeRF_High-Fidelity_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09682 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.html | CVPR 2023 | null |
NAR-Former: Neural Architecture Representation Learning Towards Holistic Attributes Prediction | Yun Yi, Haokui Zhang, Wenze Hu, Nannan Wang, Xiaoyu Wang | With the wide and deep adoption of deep learning models in real applications, there is an increasing need to model and learn the representations of the neural networks themselves. These models can be used to estimate attributes of different neural network architectures such as the accuracy and latency, without running the actual training or inference tasks. In this paper, we propose a neural architecture representation model that can be used to estimate these attributes holistically. Specifically, we first propose a simple and effective tokenizer to encode both the operation and topology information of a neural network into a single sequence. Then, we design a multi-stage fusion transformer to build a compact vector representation from the converted sequence. For efficient model training, we further propose an information flow consistency augmentation and correspondingly design an architecture consistency loss, which brings more benefits with less augmentation samples compared with previous random augmentation strategies. Experiment results on NAS-Bench-101, NAS-Bench-201, DARTS search space and NNLQP show that our proposed framework can be used to predict the aforementioned latency and accuracy attributes of both cell architectures and whole deep neural networks, and achieves promising performance. Code is available at https://github.com/yuny220/NAR-Former. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yi_NAR-Former_Neural_Architecture_Representation_Learning_Towards_Holistic_Attributes_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yi_NAR-Former_Neural_Architecture_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_NAR-Former_Neural_Architecture_Representation_Learning_Towards_Holistic_Attributes_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_NAR-Former_Neural_Architecture_Representation_Learning_Towards_Holistic_Attributes_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Implicit 3D Human Mesh Recovery Using Consistency With Pose and Shape From Unseen-View | Hanbyel Cho, Yooshin Cho, Jaesung Ahn, Junmo Kim | From an image of a person, we can easily infer the natural 3D pose and shape of the person even if ambiguity exists. This is because we have a mental model that allows us to imagine a person's appearance at different viewing directions from a given image and utilize the consistency between them for inference. However, existing human mesh recovery methods only consider the direction in which the image was taken due to their structural limitations. Hence, we propose "Implicit 3D Human Mesh Recovery (ImpHMR)" that can implicitly imagine a person in 3D space at the feature-level via Neural Feature Fields. In ImpHMR, feature fields are generated by CNN-based image encoder for a given image. Then, the 2D feature map is volume-rendered from the feature field for a given viewing direction, and the pose and shape parameters are regressed from the feature. To utilize consistency with pose and shape from unseen-view, if there are 3D labels, the model predicts results including the silhouette from an arbitrary direction and makes it equal to the rotated ground-truth. In the case of only 2D labels, we perform self-supervised learning through the constraint that the pose and shape parameters inferred from different directions should be the same. Extensive evaluations show the efficacy of the proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_Implicit_3D_Human_Mesh_Recovery_Using_Consistency_With_Pose_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cho_Implicit_3D_Human_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Implicit_3D_Human_Mesh_Recovery_Using_Consistency_With_Pose_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Implicit_3D_Human_Mesh_Recovery_Using_Consistency_With_Pose_and_CVPR_2023_paper.html | CVPR 2023 | null |
UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration | Jingyi Zhang, Jiaxing Huang, Xiaoqin Zhang, Shijian Lu | Domain adaptive panoptic segmentation aims to mitigate data annotation challenge by leveraging off-the-shelf annotated data in one or multiple related source domains. However, existing studies employ two separate networks for instance segmentation and semantic segmentation which lead to excessive network parameters as well as complicated and computationally intensive training and inference processes. We design UniDAformer, a unified domain adaptive panoptic segmentation transformer that is simple but can achieve domain adaptive instance segmentation and semantic segmentation simultaneously within a single network. UniDAformer introduces Hierarchical Mask Calibration (HMC) that rectifies inaccurate predictions at the level of regions, superpixels and pixels via online self-training on the fly. It has three unique features: 1) it enables unified domain adaptive panoptic adaptation; 2) it mitigates false predictions and improves domain adaptive panoptic segmentation effectively; 3) it is end-to-end trainable with a much simpler training and inference pipeline. Extensive experiments over multiple public benchmarks show that UniDAformer achieves superior domain adaptive panoptic segmentation as compared with the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_UniDAformer_Unified_Domain_Adaptive_Panoptic_Segmentation_Transformer_via_Hierarchical_Mask_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_UniDAformer_Unified_Domain_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.15083 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_UniDAformer_Unified_Domain_Adaptive_Panoptic_Segmentation_Transformer_via_Hierarchical_Mask_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_UniDAformer_Unified_Domain_Adaptive_Panoptic_Segmentation_Transformer_via_Hierarchical_Mask_CVPR_2023_paper.html | CVPR 2023 | null |
Non-Contrastive Learning Meets Language-Image Pre-Training | Jinghao Zhou, Li Dong, Zhe Gan, Lijuan Wang, Furu Wei | Contrastive language-image pre-training (CLIP) serves as a de-facto standard to align images and texts. Nonetheless, the loose correlation between images and texts of web-crawled data renders the contrastive objective data inefficient and craving for a large training batch size. In this work, we explore the validity of non-contrastive language-image pre-training (nCLIP) and study whether nice properties exhibited in visual self-supervised models can emerge. We empirically observe that the non-contrastive objective nourishes representation learning while sufficiently underperforming under zero-shot recognition. Based on the above study, we further introduce xCLIP, a multi-tasking framework combining CLIP and nCLIP, and show that nCLIP aids CLIP in enhancing feature semantics. The synergy between two objectives lets xCLIP enjoy the best of both worlds: superior performance in both zero-shot transfer and representation learning. Systematic evaluation is conducted spanning a wide variety of downstream tasks including zero-shot classification, out-of-domain classification, retrieval, visual representation learning, and textual representation learning, showcasing a consistent performance gain and validating the effectiveness of xCLIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Non-Contrastive_Learning_Meets_Language-Image_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Non-Contrastive_Learning_Meets_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.09304 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Non-Contrastive_Learning_Meets_Language-Image_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Non-Contrastive_Learning_Meets_Language-Image_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Teaching Structured Vision & Language Concepts to Vision & Language Models | Sivan Doveh, Assaf Arbelle, Sivan Harary, Eli Schwartz, Roei Herzig, Raja Giryes, Rogerio Feris, Rameswar Panda, Shimon Ullman, Leonid Karlinsky | Vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a challenge. We introduce the collective notion of Structured Vision & Language Concepts (SVLC) which includes object attributes, relations, and states which are present in the text and visible in the image. Recent studies have shown that even the best VL models struggle with SVLC. A possible way of fixing this issue is by collecting dedicated datasets for teaching each SVLC type, yet this might be expensive and time-consuming. Instead, we propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs that makes more effective use of existing VL pre-training datasets and does not require any additional data. While automatic understanding of image structure still remains largely unsolved, language structure is much better modeled and understood, allowing for its effective utilization in teaching VL models. In this paper, we propose various techniques based on language structure understanding that can be used to manipulate the textual part of off-the-shelf paired VL datasets. VL models trained with the updated data exhibit a significant improvement of up to 15% in their SVLC understanding with only a mild degradation in their zero-shot capabilities both when training from scratch or fine-tuning a pre-trained model. Our code and pretrained models are available at: https://github.com/SivanDoveh/TSVLC | https://openaccess.thecvf.com/content/CVPR2023/papers/Doveh_Teaching_Structured_Vision__Language_Concepts_to_Vision__Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Doveh_Teaching_Structured_Vision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Doveh_Teaching_Structured_Vision__Language_Concepts_to_Vision__Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Doveh_Teaching_Structured_Vision__Language_Concepts_to_Vision__Language_CVPR_2023_paper.html | CVPR 2023 | null |
Teleidoscopic Imaging System for Microscale 3D Shape Reconstruction | Ryo Kawahara, Meng-Yu Jennifer Kuo, Shohei Nobuhara | This paper proposes a practical method of microscale 3D shape capturing by a teleidoscopic imaging system. The main challenge in microscale 3D shape reconstruction is to capture the target from multiple viewpoints with a large enough depth-of-field. Our idea is to employ a teleidoscopic measurement system consisting of three planar mirrors and monocentric lens. The planar mirrors virtually define multiple viewpoints by multiple reflections, and the monocentric lens realizes a high magnification with less blurry and surround view even in closeup imaging. Our contributions include, a structured ray-pixel camera model which handles refractive and reflective projection rays efficiently, analytical evaluations of depth of field of our teleidoscopic imaging system, and a practical calibration algorithm of the teleidoscppic imaging system. Evaluations with real images prove the concept of our measurement system. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kawahara_Teleidoscopic_Imaging_System_for_Microscale_3D_Shape_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kawahara_Teleidoscopic_Imaging_System_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kawahara_Teleidoscopic_Imaging_System_for_Microscale_3D_Shape_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kawahara_Teleidoscopic_Imaging_System_for_Microscale_3D_Shape_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
UV Volumes for Real-Time Rendering of Editable Free-View Human Performance | Yue Chen, Xuan Wang, Xingyu Chen, Qi Zhang, Xiaoyu Li, Yu Guo, Jue Wang, Fei Wang | Neural volume rendering enables photo-realistic renderings of a human performer in free-view, a critical task in immersive VR/AR applications. But the practice is severely limited by high computational costs in the rendering process. To solve this problem, we propose the UV Volumes, a new approach that can render an editable free-view video of a human performer in real-time. It separates the high-frequency (i.e., non-smooth) human appearance from the 3D volume, and encodes them into 2D neural texture stacks (NTS). The smooth UV volumes allow much smaller and shallower neural networks to obtain densities and texture coordinates in 3D while capturing detailed appearance in 2D NTS. For editability, the mapping between the parameterized human model and the smooth texture coordinates allows us a better generalization on novel poses and shapes. Furthermore, the use of NTS enables interesting applications, e.g., retexturing. Extensive experiments on CMU Panoptic, ZJU Mocap, and H36M datasets show that our model can render 960 x 540 images in 30FPS on average with comparable photo-realism to state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_UV_Volumes_for_Real-Time_Rendering_of_Editable_Free-View_Human_Performance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_UV_Volumes_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2203.14402 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_UV_Volumes_for_Real-Time_Rendering_of_Editable_Free-View_Human_Performance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_UV_Volumes_for_Real-Time_Rendering_of_Editable_Free-View_Human_Performance_CVPR_2023_paper.html | CVPR 2023 | null |
NULL-Text Inversion for Editing Real Images Using Guided Diffusion Models | Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, Daniel Cohen-Or | Recent large-scale text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing tools. To edit a real image using these state-of-the-art tools, one must first invert the image with a meaningful text prompt into the pretrained model's domain. In this paper, we introduce an accurate inversion technique and thus facilitate an intuitive text-based modification of the image. Our proposed inversion consists of two key novel components: (i) Pivotal inversion for diffusion models. While current methods aim at mapping random noise samples to a single input image, we use a single pivotal noise vector for each timestamp and optimize around it. We recognize that a direct DDIM inversion is inadequate on its own, but does provide a rather good anchor for our optimization. (ii) NULL-text optimization, where we only modify the unconditional textual embedding that is used for classifier-free guidance, rather than the input text embedding. This allows for keeping both the model weights and the conditional embedding intact and hence enables applying prompt-based editing while avoiding the cumbersome tuning of the model's weights. Our Null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and various prompt editing, showing high-fidelity editing of real images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mokady_NULL-Text_Inversion_for_Editing_Real_Images_Using_Guided_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mokady_NULL-Text_Inversion_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09794 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mokady_NULL-Text_Inversion_for_Editing_Real_Images_Using_Guided_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mokady_NULL-Text_Inversion_for_Editing_Real_Images_Using_Guided_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
JacobiNeRF: NeRF Shaping With Mutual Information Gradients | Xiaomeng Xu, Yanchao Yang, Kaichun Mo, Boxiao Pan, Li Yi, Leonidas Guibas | We propose a method that trains a neural radiance field (NeRF) to encode not only the appearance of the scene but also semantic correlations between scene points, regions, or entities -- aiming to capture their mutual co-variation patterns. In contrast to the traditional first-order photometric reconstruction objective, our method explicitly regularizes the learning dynamics to align the Jacobians of highly-correlated entities, which proves to maximize the mutual information between them under random scene perturbations. By paying attention to this second-order information, we can shape a NeRF to express semantically meaningful synergies when the network weights are changed by a delta along the gradient of a single entity, region, or even a point. To demonstrate the merit of this mutual information modeling, we leverage the coordinated behavior of scene entities that emerges from our shaping to perform label propagation for semantic and instance segmentation. Our experiments show that a JacobiNeRF is more efficient in propagating annotations among 2D pixels and 3D points compared to NeRFs without mutual information shaping, especially in extremely sparse label regimes -- thus reducing annotation burden. The same machinery can further be used for entity selection or scene modifications. Our code is available at https://github.com/xxm19/jacobinerf. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_JacobiNeRF_NeRF_Shaping_With_Mutual_Information_Gradients_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_JacobiNeRF_NeRF_Shaping_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00341 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_JacobiNeRF_NeRF_Shaping_With_Mutual_Information_Gradients_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_JacobiNeRF_NeRF_Shaping_With_Mutual_Information_Gradients_CVPR_2023_paper.html | CVPR 2023 | null |
Selective Structured State-Spaces for Long-Form Video Understanding | Jue Wang, Wentao Zhu, Pichao Wang, Xiang Yu, Linda Liu, Mohamed Omar, Raffay Hamid | Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence (S4) model with its linear complexity offers a promising direction in this space. However, we demonstrate that treating all image-tokens equally as done by S4 model can adversely affect its efficiency and accuracy. To address this limitation, we present a novel Selective S4 (i.e., S5) model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos. Unlike previous mask-based token reduction methods used in transformers, our S5 model avoids the dense self-attention calculation by making use of the guidance of the momentum-updated S4 model. This enables our model to efficiently discard less informative tokens and adapt to various long-form video understanding tasks more effectively. However, as is the case for most token reduction methods, the informative image tokens could be dropped incorrectly. To improve the robustness and the temporal horizon of our model, we propose a novel long-short masked contrastive learning (LSMCL) approach that enables our model to predict longer temporal context using shorter input videos. We present extensive comparative results using three challenging long-form video understanding datasets (LVU, COIN and Breakfast), demonstrating that our approach consistently outperforms the previous state-of-the-art S4 model by up to 9.6% accuracy while reducing its memory footprint by 23%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.14526 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Set Representation Learning Through Combinatorial Embedding | Geeho Kim, Junoh Kang, Bohyung Han | Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable. We are interested in identifying novel concepts in a dataset through representation learning based on both labeled and unlabeled examples, and extending the horizon of recognition to both known and novel classes. To address this challenging task, we propose a combinatorial learning approach, which naturally clusters the examples in unseen classes using the compositional knowledge given by multiple supervised meta-classifiers on heterogeneous label spaces. The representations given by the combinatorial embedding are made more robust by unsupervised pairwise relation learning. The proposed algorithm discovers novel concepts via a joint optimization for enhancing the discrimitiveness of unseen classes as well as learning the representations of known classes generalizable to novel ones. Our extensive experiments demonstrate remarkable performance gains by the proposed approach on public datasets for image retrieval and image categorization with novel class discovery. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Open-Set_Representation_Learning_Through_Combinatorial_Embedding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Open-Set_Representation_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2106.15278 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Open-Set_Representation_Learning_Through_Combinatorial_Embedding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Open-Set_Representation_Learning_Through_Combinatorial_Embedding_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-View Stereo Representation Revist: Region-Aware MVSNet | Yisu Zhang, Jianke Zhu, Lixiang Lin | Deep learning-based multi-view stereo has emerged as a powerful paradigm for reconstructing the complete geometrically-detailed objects from multi-views. Most of the existing approaches only estimate the pixel-wise depth value by minimizing the gap between the predicted point and the intersection of ray and surface, which usually ignore the surface topology. It is essential to the textureless regions and surface boundary that cannot be properly reconstructed.To address this issue, we suggest to take advantage of point-to-surface distance so that the model is able to perceive a wider range of surfaces. To this end, we predict the distance volume from cost volume to estimate the signed distance of points around the surface. Our proposed RA-MVSNet is patch-awared, since the perception range is enhanced by associating hypothetical planes with a patch of surface. Therefore, it could increase the completion of textureless regions and reduce the outliers at the boundary. Moreover, the mesh topologies with fine details can be generated by the introduced distance volume. Comparing to the conventional deep learning-based multi-view stereo methods, our proposed RA-MVSNet approach obtains more complete reconstruction results by taking advantage of signed distance supervision. The experiments on both the DTU and Tanks & Temples datasets demonstrate that our proposed approach achieves the state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Multi-View_Stereo_Representation_Revist_Region-Aware_MVSNet_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Multi-View_Stereo_Representation_Revist_Region-Aware_MVSNet_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Multi-View_Stereo_Representation_Revist_Region-Aware_MVSNet_CVPR_2023_paper.html | CVPR 2023 | null |
A Unified HDR Imaging Method With Pixel and Patch Level | Qingsen Yan, Weiye Chen, Song Zhang, Yu Zhu, Jinqiu Sun, Yanning Zhang | Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To generate visually pleasing HDR images in various cases, we propose a hybrid HDR deghosting network, called HyHDRNet, to learn the complicated relationship between reference and non-reference images. The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork. Specifically, to effectively avoid ghosting from the source, the content alignment subnetwork uses patch aggregation and ghost attention to integrate similar content from other non-reference images with patch level and suppress undesired components with pixel level. To achieve mutual guidance between patch-level and pixel-level, we leverage a gating module to sufficiently swap useful information both in ghosted and saturated regions. Furthermore, to obtain a high-quality HDR image, the Transformer-based fusion subnetwork uses a Residual Deformable Transformer Block (RDTB) to adaptively merge information for different exposed regions. We examined the proposed method on four widely used public HDR image deghosting datasets. Experiments demonstrate that HyHDRNet outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_A_Unified_HDR_Imaging_Method_With_Pixel_and_Patch_Level_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.06943 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_A_Unified_HDR_Imaging_Method_With_Pixel_and_Patch_Level_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_A_Unified_HDR_Imaging_Method_With_Pixel_and_Patch_Level_CVPR_2023_paper.html | CVPR 2023 | null |
Motion Information Propagation for Neural Video Compression | Linfeng Qi, Jiahao Li, Bin Li, Houqiang Li, Yan Lu | In most existing neural video codecs, the information flow therein is uni-directional, where only motion coding provides motion vectors for frame coding. In this paper, we argue that, through information interactions, the synergy between motion coding and frame coding can be achieved. We effectively introduce bi-directional information interactions between motion coding and frame coding via our Motion Information Propagation. When generating the temporal contexts for frame coding, the high-dimension motion feature from the motion decoder serves as motion guidance to mitigate the alignment errors. Meanwhile, besides assisting frame coding at the current time step, the feature from context generation will be propagated as motion condition when coding the subsequent motion latent. Through the cycle of such interactions, feature propagation on motion coding is built, strengthening the capacity of exploiting long-range temporal correlation. In addition, we propose hybrid context generation to exploit the multi-scale context features and provide better motion condition. Experiments show that our method can achieve 12.9% bit rate saving over the previous SOTA neural video codec. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qi_Motion_Information_Propagation_for_Neural_Video_Compression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qi_Motion_Information_Propagation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qi_Motion_Information_Propagation_for_Neural_Video_Compression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qi_Motion_Information_Propagation_for_Neural_Video_Compression_CVPR_2023_paper.html | CVPR 2023 | null |
Accelerated Coordinate Encoding: Learning to Relocalize in Minutes Using RGB and Poses | Eric Brachmann, Tommaso Cavallari, Victor Adrian Prisacariu | Learning-based visual relocalizers exhibit leading pose accuracy, but require hours or days of training. Since training needs to happen on each new scene again, long training times make learning-based relocalization impractical for most applications, despite its promise of high accuracy. In this paper we show how such a system can actually achieve the same accuracy in less than 5 minutes. We start from the obvious: a relocalization network can be split in a scene-agnostic feature backbone, and a scene-specific prediction head. Less obvious: using an MLP prediction head allows us to optimize across thousands of view points simultaneously in each single training iteration. This leads to stable and extremely fast convergence. Furthermore, we substitute effective but slow end-to-end training using a robust pose solver with a curriculum over a reprojection loss. Our approach does not require privileged knowledge, such a depth maps or a 3D model, for speedy training. Overall, our approach is up to 300x faster in mapping than state-of-the-art scene coordinate regression, while keeping accuracy on par. Code is available: https://nianticlabs.github.io/ace | https://openaccess.thecvf.com/content/CVPR2023/papers/Brachmann_Accelerated_Coordinate_Encoding_Learning_to_Relocalize_in_Minutes_Using_RGB_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Brachmann_Accelerated_Coordinate_Encoding_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Brachmann_Accelerated_Coordinate_Encoding_Learning_to_Relocalize_in_Minutes_Using_RGB_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Brachmann_Accelerated_Coordinate_Encoding_Learning_to_Relocalize_in_Minutes_Using_RGB_CVPR_2023_paper.html | CVPR 2023 | null |
Switchable Representation Learning Framework With Self-Compatibility | Shengsen Wu, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Ling-Yu Duan | Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected to deploy models with different capacities adapting to the resource constraints, which requires features extracted by these models to be aligned in the metric space. The method to achieve feature alignments is called "compatible learning". Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models. We propose a Switchable representation learning Framework with Self-Compatibility (SFSC). SFSC generates a series of compatible sub-models with different capacities through one training process. The optimization of sub-models faces gradients conflict, and we mitigate this problem from the perspective of the magnitude and direction. We adjust the priorities of sub-models dynamically through uncertainty estimation to co-optimize sub-models properly. Besides, the gradients with conflicting directions are projected to avoid mutual interference. SFSC achieves state-of-the-art performance on the evaluated datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Switchable_Representation_Learning_Framework_With_Self-Compatibility_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2206.08289 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Switchable_Representation_Learning_Framework_With_Self-Compatibility_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Switchable_Representation_Learning_Framework_With_Self-Compatibility_CVPR_2023_paper.html | CVPR 2023 | null |
Partial Network Cloning | Jingwen Ye, Songhua Liu, Xinchao Wang | In this paper, we study a novel task that enables partial knowledge transfer from pre-trained models, which we term as Partial Network Cloning (PNC). Unlike prior methods that update all or at least part of the parameters in the target network throughout the knowledge transfer process, PNC conducts partial parametric "cloning" from a source network and then injects the cloned module to the target, without modifying its parameters. Thanks to the transferred module, the target network is expected to gain additional functionality, such as inference on new classes; whenever needed, the cloned module can be readily removed from the target, with its original parameters and competence kept intact. Specifically, we introduce an innovative learning scheme that allows us to identify simultaneously the component to be cloned from the source and the position to be inserted within the target network, so as to ensure the optimal performance. Experimental results on several datasets demonstrate that, our method yields a significant improvement of 5% in accuracy and 50% in locality when compared with parameter-tuning based methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_Partial_Network_Cloning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_Partial_Network_Cloning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Partial_Network_Cloning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Partial_Network_Cloning_CVPR_2023_paper.html | CVPR 2023 | null |
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors | Yuang Zhang, Tiancai Wang, Xiangyu Zhang | In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, e.g. MOTR and TrackFormer are inferior to their tracking-by-detection counterparts mainly due to their poor detection performance. We aim to improve MOTR by elegantly incorporating an extra object detector. We first adopt the anchor formulation of queries and then use an extra object detector to generate proposals as anchors, providing detection prior to MOTR. The simple modification greatly eases the conflict between joint learning detection and association tasks in MOTR. MOTRv2 keeps the end-to-end feature and scales well on large-scale benchmarks. MOTRv2 achieves the top performance (73.4% HOTA) among all existing methods on the DanceTrack dataset. Moreover, MOTRv2 reaches state-of-the-art performance on the BDD100K dataset. We hope this simple and effective pipeline can provide some new insights to the end-to-end MOT community. The code will be released in the near future. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_MOTRv2_Bootstrapping_End-to-End_Multi-Object_Tracking_by_Pretrained_Object_Detectors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_MOTRv2_Bootstrapping_End-to-End_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09791 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MOTRv2_Bootstrapping_End-to-End_Multi-Object_Tracking_by_Pretrained_Object_Detectors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MOTRv2_Bootstrapping_End-to-End_Multi-Object_Tracking_by_Pretrained_Object_Detectors_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Dual-Lens Super-Resolution | Ruikang Xu, Mingde Yao, Zhiwei Xiong | The asymmetric dual-lens configuration is commonly available on mobile devices nowadays, which naturally stores a pair of wide-angle and telephoto images of the same scene to support realistic super-resolution (SR). Even on the same device, however, the degradation for modeling realistic SR is image-specific due to the unknown acquisition process (e.g., tiny camera motion). In this paper, we propose a zero-shot solution for dual-lens SR (ZeDuSR), where only the dual-lens pair at test time is used to learn an image-specific SR model. As such, ZeDuSR adapts itself to the current scene without using external training data, and thus gets rid of generalization difficulty. However, there are two major challenges to achieving this goal: 1) dual-lens alignment while keeping the realistic degradation, and 2) effective usage of highly limited training data. To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair. Extensive experiments validate the superiority of ZeDuSR over existing solutions on both synthesized and real-world dual-lens datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Zero-Shot_Dual-Lens_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Zero-Shot_Dual-Lens_Super-Resolution_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Zero-Shot_Dual-Lens_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Zero-Shot_Dual-Lens_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Dynamic Radiance Fields | Yu-Lun Liu, Chen Gao, Andréas Meuleman, Hung-Yu Tseng, Ayush Saraf, Changil Kim, Yung-Yu Chuang, Johannes Kopf, Jia-Bin Huang | Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. These methods, thus, are unreliable as SfM algorithms often fail or produce erroneous poses on challenging videos with highly dynamic objects, poorly textured surfaces, and rotating camera motion. We address this issue by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). We demonstrate the robustness of our approach via extensive quantitative and qualitative experiments. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Robust_Dynamic_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Robust_Dynamic_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02239 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Robust_Dynamic_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Robust_Dynamic_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Vision-and-Language Navigation by Generating Future-View Image Semantics | Jialu Li, Mohit Bansal | Vision-and-Language Navigation (VLN) is the task that requires an agent to navigate through the environment based on natural language instructions. At each step, the agent takes the next action by selecting from a set of navigable locations. In this paper, we aim to take one step further and explore whether the agent can benefit from generating the potential future view during navigation. Intuitively, humans will have an expectation of how the future environment will look like, based on the natural language instructions and surrounding views, which will aid correct navigation. Hence, to equip the agent with this ability to generate the semantics of future navigation views, we first propose three proxy tasks during the agent's in-domain pre-training: Masked Panorama Modeling (MPM), Masked Trajectory Modeling (MTM), and Action Prediction with Image Generation (APIG). These three objectives teach the model to predict missing views in a panorama (MPM), predict missing steps in the full trajectory (MTM), and generate the next view based on the full instruction and navigation history (APIG), respectively. We then fine-tune the agent on the VLN task with an auxiliary loss that minimizes the difference between the view semantics generated by the agent and the ground truth view semantics of the next step. Empirically, our VLN-SIG achieves the new state-of-the-art on both the Room-to-Room dataset and the CVDN dataset. We further show that our agent learns to fill in missing patches in future views qualitatively, which brings more interpretability over agents' predicted actions. Lastly, we demonstrate that learning to predict future view semantics also enables the agent to have better performance on longer paths. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Improving_Vision-and-Language_Navigation_by_Generating_Future-View_Image_Semantics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Improving_Vision-and-Language_Navigation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04907 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Improving_Vision-and-Language_Navigation_by_Generating_Future-View_Image_Semantics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Improving_Vision-and-Language_Navigation_by_Generating_Future-View_Image_Semantics_CVPR_2023_paper.html | CVPR 2023 | null |
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation | Karthik Shetty, Annette Birkhold, Srikrishna Jaganathan, Norbert Strobel, Markus Kowarschik, Andreas Maier, Bernhard Egger | We introduce PLIKS (Pseudo-Linear Inverse Kinematic Solver) for reconstruction of a 3D mesh of the human body from a single 2D image. Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences. We approach the task as a model-in-the-loop optimization problem. PLIKS is built on a linearized formulation of the parametric SMPL model. Using PLIKS, we can analytically reconstruct the human model via 2D pixel-aligned vertices. This enables us with the flexibility to use accurate camera calibration information when available. PLIKS offers an easy way to introduce additional constraints such as shape and translation. We present quantitative evaluations which confirm that PLIKS achieves more accurate reconstruction with greater than 10% improvement compared to other state-of-the-art methods with respect to the standard 3D human pose and shape benchmarks while also obtaining a reconstruction error improvement of 12.9 mm on the newer AGORA dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shetty_PLIKS_A_Pseudo-Linear_Inverse_Kinematic_Solver_for_3D_Human_Body_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shetty_PLIKS_A_Pseudo-Linear_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11734 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shetty_PLIKS_A_Pseudo-Linear_Inverse_Kinematic_Solver_for_3D_Human_Body_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shetty_PLIKS_A_Pseudo-Linear_Inverse_Kinematic_Solver_for_3D_Human_Body_CVPR_2023_paper.html | CVPR 2023 | null |
Promoting Semantic Connectivity: Dual Nearest Neighbors Contrastive Learning for Unsupervised Domain Generalization | Yuchen Liu, Yaoming Wang, Yabo Chen, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong | Domain Generalization (DG) has achieved great success in generalizing knowledge from source domains to unseen target domains. However, current DG methods rely heavily on labeled source data, which are usually costly and unavailable. Since unlabeled data are far more accessible, we study a more practical unsupervised domain generalization (UDG) problem. Learning invariant visual representation from different views, i.e., contrastive learning, promises well semantic features for in-domain unsupervised learning. However, it fails in cross-domain scenarios. In this paper, we first delve into the failure of vanilla contrastive learning and point out that semantic connectivity is the key to UDG. Specifically, suppressing the intra-domain connectivity and encouraging the intra-class connectivity help to learn the domain-invariant semantic information. Then, we propose a novel unsupervised domain generalization approach, namely Dual Nearest Neighbors contrastive learning with strong Augmentation (DN^2A). Our DN^2A leverages strong augmentations to suppress the intra-domain connectivity and proposes a novel dual nearest neighbors search strategy to find trustworthy cross domain neighbors along with in-domain neighbors to encourage the intra-class connectivity. Experimental results demonstrate that our DN^2A outperforms the state-of-the-art by a large margin, e.g., 12.01% and 13.11% accuracy gain with only 1% labels for linear evaluation on PACS and DomainNet, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Promoting_Semantic_Connectivity_Dual_Nearest_Neighbors_Contrastive_Learning_for_Unsupervised_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Promoting_Semantic_Connectivity_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Promoting_Semantic_Connectivity_Dual_Nearest_Neighbors_Contrastive_Learning_for_Unsupervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Promoting_Semantic_Connectivity_Dual_Nearest_Neighbors_Contrastive_Learning_for_Unsupervised_CVPR_2023_paper.html | CVPR 2023 | null |
Interactive Segmentation of Radiance Fields | Rahul Goel, Dhawal Sirikonda, Saurabh Saini, P. J. Narayanan | Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it. Mixed reality on personal spaces needs understanding and manipulating scenes represented as RFs, with semantic segmentation of objects as an important step. Prior segmentation efforts show promise but don't scale to complex objects with diverse appearance. We present the ISRF method to interactively segment objects with fine structure and appearance. Nearest neighbor feature matching using distilled semantic features identifies high-confidence seed regions. Bilateral search in a joint spatio-semantic space grows the region to recover accurate segmentation. We show state-of-the-art results of segmenting objects from RFs and compositing them to another scene, changing appearance, etc., and an interactive segmentation tool that others can use. | https://openaccess.thecvf.com/content/CVPR2023/papers/Goel_Interactive_Segmentation_of_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Goel_Interactive_Segmentation_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.13545 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Goel_Interactive_Segmentation_of_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Goel_Interactive_Segmentation_of_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction | Zerui Chen, Shizhe Chen, Cordelia Schmid, Ivan Laptev | Signed distance functions (SDFs) is an attractive framework that has recently shown promising results for 3D shape reconstruction from images. SDFs seamlessly generalize to different shape resolutions and topologies but lack explicit modelling of the underlying 3D geometry. In this work, we exploit the hand structure and use it as guidance for SDF-based shape reconstruction. In particular, we address reconstruction of hands and manipulated objects from monocular RGB images. To this end, we estimate poses of hands and objects and use them to guide 3D reconstruction. More specifically, we predict kinematic chains of pose transformations and align SDFs with highly-articulated hand poses. We improve the visual features of 3D points with geometry alignment and further leverage temporal information to enhance the robustness to occlusion and motion blurs. We conduct extensive experiments on the challenging ObMan and DexYCB benchmarks and demonstrate significant improvements of the proposed method over the state of the art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_gSDF_Geometry-Driven_Signed_Distance_Functions_for_3D_Hand-Object_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_gSDF_Geometry-Driven_Signed_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11970 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_gSDF_Geometry-Driven_Signed_Distance_Functions_for_3D_Hand-Object_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_gSDF_Geometry-Driven_Signed_Distance_Functions_for_3D_Hand-Object_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions | Tobias Kalb, Jürgen Beyerer | Deep neural networks for scene perception in automated vehicles achieve excellent results for the domains they were trained on. However, in real-world conditions, the domain of operation and its underlying data distribution are subject to change. Adverse weather conditions, in particular, can significantly decrease model performance when such data are not available during training. Additionally, when a model is incrementally adapted to a new domain, it suffers from catastrophic forgetting, causing a significant drop in performance on previously observed domains. Despite recent progress in reducing catastrophic forgetting, its causes and effects remain obscure. Therefore, we study how the representations of semantic segmentation models are affected during domain-incremental learning in adverse weather conditions. Our experiments and representational analyses indicate that catastrophic forgetting is primarily caused by changes to low-level features in domain-incremental learning and that learning more general features on the source domain using pre-training and image augmentations leads to efficient feature reuse in subsequent tasks, which drastically reduces catastrophic forgetting. These findings highlight the importance of methods that facilitate generalized features for effective continual learning algorithms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kalb_Principles_of_Forgetting_in_Domain-Incremental_Semantic_Segmentation_in_Adverse_Weather_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kalb_Principles_of_Forgetting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14115 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kalb_Principles_of_Forgetting_in_Domain-Incremental_Semantic_Segmentation_in_Adverse_Weather_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kalb_Principles_of_Forgetting_in_Domain-Incremental_Semantic_Segmentation_in_Adverse_Weather_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Texture Synthesis With Guided Correspondence | Yang Zhou, Kaijian Chen, Rongjun Xiao, Hui Huang | Markov random fields (MRFs) are the cornerstone of classical approaches to example-based texture synthesis. Yet, it is not fully valued in the deep learning era. This paper aims to re-promote the combination of MRFs and neural networks, i.e., the CNNMRF model, for texture synthesis, with two key observations made. We first propose to compute the Guided Correspondence Distance in the nearest neighbor search, based on which a Guided Correspondence loss is defined to measure the similarity of the output texture to the example. Experiments show that our approach surpasses existing neural approaches in uncontrolled and controlled texture synthesis. More importantly, the Guided Correspondence loss can function as a general textural loss in, e.g., training generative networks for real-time controlled synthesis and inversion-based single-image editing. In contrast, existing textural losses, such as the Sliced Wasserstein loss, cannot work on these challenging tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Neural_Texture_Synthesis_With_Guided_Correspondence_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Neural_Texture_Synthesis_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Neural_Texture_Synthesis_With_Guided_Correspondence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Neural_Texture_Synthesis_With_Guided_Correspondence_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring and Utilizing Pattern Imbalance | Shibin Mei, Chenglong Zhao, Shengchao Yuan, Bingbing Ni | In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mei_Exploring_and_Utilizing_Pattern_Imbalance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mei_Exploring_and_Utilizing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Exploring_and_Utilizing_Pattern_Imbalance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Exploring_and_Utilizing_Pattern_Imbalance_CVPR_2023_paper.html | CVPR 2023 | null |
Are Data-Driven Explanations Robust Against Out-of-Distribution Data? | Tang Li, Fengchun Qiao, Mengmeng Ma, Xi Peng | As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantly challenged by distributional shifts. A question naturally arises: Are data-driven explanations robust against out-of-distribution data? Our empirical results show that even though predict correctly, the model might still yield unreliable explanations under distributional shifts. How to develop robust explanations against out-of-distribution data? To address this problem, we propose an end-to-end model-agnostic learning framework Distributionally Robust Explanations (DRE). The key idea is, inspired by self-supervised learning, to fully utilizes the inter-distribution information to provide supervisory signals for the learning of explanations without human annotation. Can robust explanations benefit the model's generalization capability? We conduct extensive experiments on a wide range of tasks and data types, including classification and regression on image and scientific tabular data. Our results demonstrate that the proposed method significantly improves the model's performance in terms of explanation and prediction robustness against distributional shifts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Are_Data-Driven_Explanations_Robust_Against_Out-of-Distribution_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Are_Data-Driven_Explanations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16390 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Are_Data-Driven_Explanations_Robust_Against_Out-of-Distribution_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Are_Data-Driven_Explanations_Robust_Against_Out-of-Distribution_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Top-Down Visual Attention From Analysis by Synthesis | Baifeng Shi, Trevor Darrell, Xin Wang | Current attention algorithms (e.g., self-attention) are stimulus-driven and highlight all the salient objects in an image. However, intelligent agents like humans often guide their attention based on the high-level task at hand, focusing only on task-related objects. This ability of task-guided top-down attention provides task-adaptive representation and helps the model generalize to various tasks. In this paper, we consider top-down attention from a classic Analysis-by-Synthesis (AbS) perspective of vision. Prior work indicates a functional equivalence between visual attention and sparse reconstruction; we show that an AbS visual system that optimizes a similar sparse reconstruction objective modulated by a goal-directed top-down signal naturally simulates top-down attention. We further propose Analysis-by-Synthesis Vision Transformer (AbSViT), which is a top-down modulated ViT model that variationally approximates AbS, and achieves controllable top-down attention. For real-world applications, AbSViT consistently improves over baselines on Vision-Language tasks such as VQA and zero-shot retrieval where language guides the top-down attention. AbSViT can also serve as a general backbone, improving performance on classification, semantic segmentation, and model robustness. Project page: https://sites.google.com/view/absvit. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shi_Top-Down_Visual_Attention_From_Analysis_by_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shi_Top-Down_Visual_Attention_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13043 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Top-Down_Visual_Attention_From_Analysis_by_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Top-Down_Visual_Attention_From_Analysis_by_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Fine-Grained Image Forgery Detection and Localization | Xiao Guo, Xiaohong Liu, Zhiyuan Ren, Steven Grosz, Iacopo Masi, Xiaoming Liu | Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then we perform fine-grained classification at these levels using the hierarchical dependency between them. As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation. Our proposed IFDL framework contains three components: multi-branch feature extractor, localization and classification modules. Each branch of the feature extractor learns to classify forgery attributes at one level, while localization and classification modules segment the pixel-level forgery region and detect image-level forgery, respectively. Lastly, we construct a hierarchical fine-grained dataset to facilitate our study. We demonstrate the effectiveness of our method on 7 different benchmarks, for both tasks of IFDL and forgery attribute classification. Our source code and dataset can be found at https://github.com/CHELSEA234/HiFi_IFDL | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Hierarchical_Fine-Grained_Image_Forgery_Detection_and_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Hierarchical_Fine-Grained_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17111 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Hierarchical_Fine-Grained_Image_Forgery_Detection_and_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Hierarchical_Fine-Grained_Image_Forgery_Detection_and_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
CIMI4D: A Large Multimodal Climbing Motion Dataset Under Human-Scene Interactions | Ming Yan, Xin Wang, Yudi Dai, Siqi Shen, Chenglu Wen, Lan Xu, Yuexin Ma, Cheng Wang | Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off-grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an in-depth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock ClImbing MotIon on dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://www.lidarhumanmotion.net/cimi4d/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_CIMI4D_A_Large_Multimodal_Climbing_Motion_Dataset_Under_Human-Scene_Interactions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_CIMI4D_A_Large_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.17948 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_CIMI4D_A_Large_Multimodal_Climbing_Motion_Dataset_Under_Human-Scene_Interactions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_CIMI4D_A_Large_Multimodal_Climbing_Motion_Dataset_Under_Human-Scene_Interactions_CVPR_2023_paper.html | CVPR 2023 | null |
Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts | Nikolas Lamb, Cameron Palmer, Benjamin Molloy, Sean Banerjee, Natasha Kholgade Banerjee | Automated shape repair approaches currently lack access to datasets that describe real-world damaged geometry. We present Fantastic Breaks (and Where to Find Them: https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), a dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken objects, paired and geometrically aligned with complete counterparts. Fantastic Breaks contains class and material labels, proxy repair parts that join to broken meshes to generate complete meshes, and manually annotated fracture boundaries. Through a detailed analysis of fracture geometry, we reveal differences between Fantastic Breaks and synthetic fracture datasets generated using geometric and physics-based methods. We show experimental shape repair evaluation with Fantastic Breaks using multiple learning-based approaches pre-trained with synthetic datasets and re-trained with subset of Fantastic Breaks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lamb_Fantastic_Breaks_A_Dataset_of_Paired_3D_Scans_of_Real-World_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lamb_Fantastic_Breaks_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lamb_Fantastic_Breaks_A_Dataset_of_Paired_3D_Scans_of_Real-World_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lamb_Fantastic_Breaks_A_Dataset_of_Paired_3D_Scans_of_Real-World_CVPR_2023_paper.html | CVPR 2023 | null |
Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer | Agus Gunawan, Soo Ye Kim, Hyeonjun Sim, Jae-Ho Lee, Munchurl Kim | This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gunawan_Modernizing_Old_Photos_Using_Multiple_References_via_Photorealistic_Style_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gunawan_Modernizing_Old_Photos_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04461 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gunawan_Modernizing_Old_Photos_Using_Multiple_References_via_Photorealistic_Style_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gunawan_Modernizing_Old_Photos_Using_Multiple_References_via_Photorealistic_Style_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
Interactive Cartoonization With Controllable Perceptual Factors | Namhyuk Ahn, Patrick Kwon, Jihye Back, Kibeom Hong, Seungkwon Kim | Cartoonization is a task that renders natural photos into cartoon styles. Previous deep methods only have focused on end-to-end translation, disabling artists from manipulating results. To tackle this, in this work, we propose a novel solution with editing features of texture and color based on the cartoon creation process. To do that, we design a model architecture to have separate decoders, texture and color, to decouple these attributes. In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures. We also introduce an HSV color augmentation to induce the networks to generate consistent color translation. To the best of our knowledge, our work is the first method to control the cartoonization during the inferences step, generating high-quality results compared to baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ahn_Interactive_Cartoonization_With_Controllable_Perceptual_Factors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ahn_Interactive_Cartoonization_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09555 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ahn_Interactive_Cartoonization_With_Controllable_Perceptual_Factors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ahn_Interactive_Cartoonization_With_Controllable_Perceptual_Factors_CVPR_2023_paper.html | CVPR 2023 | null |