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title string | authors string | abstract string | pdf string | supp string | arXiv string | bibtex string | url string | detail_url string | tags string | string |
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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 frequentl... | 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... | 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 datase... | 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, ... | 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... | 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 chan... | 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 augmentat... | 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 dynamic... | 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 r... | 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 generat... | 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 are... | 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, rec... | 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 lead... | 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-... | 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 vis... | 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 s... | 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 ... | 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, ... | 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 ... | 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... | 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 ... | 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... | 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 ed... | 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... | 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... | 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 mode... | 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 o... | 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, wh... | 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... | 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-direct... | 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 ... | 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... | 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 "... | 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 imp... | 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... | 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 p... | 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 c... | 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 influ... | 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 do... | 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 p... | 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 a... | 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 mod... | 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 f... | 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 da... | 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? Ou... | 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 provi... | 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, w... | 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, th... | 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 o... | 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 g... | 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 pro... | 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 |
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