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Uncertainty-Aware Vision-Based Metric Cross-View Geolocalization | Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen | This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous driving, i.e. using expensive high-definition prior maps or relying entirely on the sensor data captured at runtime. We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We combine multiple vehicle datasets with aerial images from orthophoto providers on which we demonstrate the feasibility of our method. Since the ground truth poses are often inaccurate w.r.t. the aerial images, we implement a pseudo-label approach to produce more accurate ground truth poses and make them publicly available. While previous works require training data from the target region to achieve reasonable localization accuracy (i.e. same-area evaluation), our approach overcomes this limitation and outperforms previous results even in the strictly more challenging cross-area case. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region, which highlights the model's potential for global-scale application. We further integrate the uncertainty-aware predictions in a tracking framework to determine the vehicle's trajectory over time resulting in a mean position error on KITTI-360 of 0.78m. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fervers_Uncertainty-Aware_Vision-Based_Metric_Cross-View_Geolocalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fervers_Uncertainty-Aware_Vision-Based_Metric_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.12145 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fervers_Uncertainty-Aware_Vision-Based_Metric_Cross-View_Geolocalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fervers_Uncertainty-Aware_Vision-Based_Metric_Cross-View_Geolocalization_CVPR_2023_paper.html | CVPR 2023 | null |
DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering | Zongrui Li, Qian Zheng, Boxin Shi, Gang Pan, Xudong Jiang | Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_DANI-Net_Uncalibrated_Photometric_Stereo_by_Differentiable_Shadow_Handling_Anisotropic_Reflectance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_DANI-Net_Uncalibrated_Photometric_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DANI-Net_Uncalibrated_Photometric_Stereo_by_Differentiable_Shadow_Handling_Anisotropic_Reflectance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DANI-Net_Uncalibrated_Photometric_Stereo_by_Differentiable_Shadow_Handling_Anisotropic_Reflectance_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation | Dong Zhao, Shuang Wang, Qi Zang, Dou Quan, Xiutiao Ye, Licheng Jiao | Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain. The need to access labeled source data makes UDA unable to handle adaptation scenarios involving privacy, property rights protection, and confidentiality. In this paper, we focus on unsupervised model adaptation (UMA), also called source-free domain adaptation, which adapts a source-trained model to the target domain without accessing source data. We find that the online self-training method has the potential to be deployed in UMA, but the lack of source domain loss will greatly weaken the stability and adaptability of the method. We analyze the two possible reasons for the degradation of online self-training, i.e. inopportune updates of the teacher model and biased knowledge from source-trained model. Based on this, we propose a dynamic teacher update mechanism and a training-consistency based resampling strategy to improve the stability and adaptability of online self training. On multiple model adaptation benchmarks, our method obtains new state-of-the-art performance, which is comparable or even better than state-of-the-art UDA methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Towards_Better_Stability_and_Adaptability_Improve_Online_Self-Training_for_Model_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Towards_Better_Stability_and_Adaptability_Improve_Online_Self-Training_for_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Towards_Better_Stability_and_Adaptability_Improve_Online_Self-Training_for_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Continuous Landmark Detection With 3D Queries | Prashanth Chandran, Gaspard Zoss, Paulo Gotardo, Derek Bradley | Neural networks for facial landmark detection are notoriously limited to a fixed set of landmarks in a dedicated layout, which must be specified at training time. Dedicated datasets must also be hand-annotated with the corresponding landmark configuration for training. We propose the first facial landmark detection network that can predict continuous, unlimited landmarks, allowing to specify the number and location of the desired landmarks at inference time. Our method combines a simple image feature extractor with a queried landmark predictor, and the user can specify any continuous query points relative to a 3D template face mesh as input. As it is not tied to a fixed set of landmarks, our method is able to leverage all pre-existing 2D landmark datasets for training, even if they have inconsistent landmark configurations. As a result, we present a very powerful facial landmark detector that can be trained once, and can be used readily for numerous applications like 3D face reconstruction, arbitrary face segmentation, and is even compatible with helmeted mounted cameras, and therefore could vastly simplify face tracking workflows for media and entertainment applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chandran_Continuous_Landmark_Detection_With_3D_Queries_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chandran_Continuous_Landmark_Detection_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chandran_Continuous_Landmark_Detection_With_3D_Queries_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chandran_Continuous_Landmark_Detection_With_3D_Queries_CVPR_2023_paper.html | CVPR 2023 | null |
Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate | Kiarash Mohammadi, He Zhao, Mengyao Zhai, Frederick Tung | In many real-world settings, the critical class is rare and a missed detection carries a disproportionately high cost. For example, tumors are rare and a false negative diagnosis could have severe consequences on treatment outcomes; fraudulent banking transactions are rare and an undetected occurrence could result in significant losses or legal penalties. In such contexts, systems are often operated at a high true positive rate, which may require tolerating high false positives. In this paper, we present a novel approach to address the challenge of minimizing false positives for systems that need to operate at a high true positive rate. We propose a ranking-based regularization (RankReg) approach that is easy to implement, and show empirically that it not only effectively reduces false positives, but also complements conventional imbalanced learning losses. With this novel technique in hand, we conduct a series of experiments on three broadly explored datasets (CIFAR-10&100 and Melanoma) and show that our approach lifts the previous state-of-the-art performance by notable margins. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mohammadi_Ranking_Regularization_for_Critical_Rare_Classes_Minimizing_False_Positives_at_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mohammadi_Ranking_Regularization_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mohammadi_Ranking_Regularization_for_Critical_Rare_Classes_Minimizing_False_Positives_at_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mohammadi_Ranking_Regularization_for_Critical_Rare_Classes_Minimizing_False_Positives_at_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Gradient Projection Continual Learning: Stability / Plasticity Feature Space Decoupling | Zhen Zhao, Zhizhong Zhang, Xin Tan, Jun Liu, Yanyun Qu, Yuan Xie, Lizhuang Ma | Continual learning aims to incrementally learn novel classes over time, while not forgetting the learned knowledge. Recent studies have found that learning would not forget if the updated gradient is orthogonal to the feature space. However, previous approaches require the gradient to be fully orthogonal to the whole feature space, leading to poor plasticity, as the feasible gradient direction becomes narrow when the tasks continually come, i.e., feature space is unlimitedly expanded. In this paper, we propose a space decoupling (SD) algorithm to decouple the feature space into a pair of complementary subspaces, i.e., the stability space I, and the plasticity space R. I is established by conducting space intersection between the historic and current feature space, and thus I contains more task-shared bases. R is constructed by seeking the orthogonal complementary subspace of I, and thus R mainly contains more task-specific bases. By putting the distinguishing constraints on R and I, our method achieves a better balance between stability and plasticity. Extensive experiments are conducted by applying SD to gradient projection baselines, and show SD is model-agnostic and achieves SOTA results on publicly available datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Rethinking_Gradient_Projection_Continual_Learning_Stability__Plasticity_Feature_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Rethinking_Gradient_Projection_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Rethinking_Gradient_Projection_Continual_Learning_Stability__Plasticity_Feature_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Rethinking_Gradient_Projection_Continual_Learning_Stability__Plasticity_Feature_Space_CVPR_2023_paper.html | CVPR 2023 | null |
Joint HDR Denoising and Fusion: A Real-World Mobile HDR Image Dataset | Shuaizheng Liu, Xindong Zhang, Lingchen Sun, Zhetong Liang, Hui Zeng, Lei Zhang | Mobile phones have become a ubiquitous and indispensable photographing device in our daily life, while the small aperture and sensor size make mobile phones more susceptible to noise and over-saturation, resulting in low dynamic range (LDR) and low image quality. It is thus crucial to develop high dynamic range (HDR) imaging techniques for mobile phones. Unfortunately, the existing HDR image datasets are mostly constructed by DSLR cameras in daytime, limiting their applicability to the study of HDR imaging for mobile phones. In this work, we develop, for the first time to our best knowledge, an HDR image dataset by using mobile phone cameras, namely Mobile-HDR dataset. Specifically, we utilize three mobile phone cameras to collect paired LDR-HDR images in the raw image domain, covering both daytime and nighttime scenes with different noise levels. We then propose a transformer based model with a pyramid cross-attention alignment module to aggregate highly correlated features from different exposure frames to perform joint HDR denoising and fusion. Experiments validate the advantages of our dataset and our method on mobile HDR imaging. Dataset and codes are available at https://github.com/shuaizhengliu/Joint-HDRDN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Joint_HDR_Denoising_and_Fusion_A_Real-World_Mobile_HDR_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Joint_HDR_Denoising_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Joint_HDR_Denoising_and_Fusion_A_Real-World_Mobile_HDR_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Joint_HDR_Denoising_and_Fusion_A_Real-World_Mobile_HDR_Image_CVPR_2023_paper.html | CVPR 2023 | null |
FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer | Zhijian Liu, Xinyu Yang, Haotian Tang, Shang Yang, Song Han | Transformer, as an alternative to CNN, has been proven effective in many modalities (e.g., texts and images). For 3D point cloud transformers, existing efforts focus primarily on pushing their accuracy to the state-of-the-art level. However, their latency lags behind sparse convolution-based models (3x slower), hindering their usage in resource-constrained, latency-sensitive applications (such as autonomous driving). This inefficiency comes from point clouds' sparse and irregular nature, whereas transformers are designed for dense, regular workloads. This paper presents FlatFormer to close this latency gap by trading spatial proximity for better computational regularity. We first flatten the point cloud with window-based sorting and partition points into groups of equal sizes rather than windows of equal shapes. This effectively avoids expensive structuring and padding overheads. We then apply self-attention within groups to extract local features, alternate sorting axis to gather features from different directions, and shift windows to exchange features across groups. FlatFormer delivers state-of-the-art accuracy on Waymo Open Dataset with 4.6x speedup over (transformer-based) SST and 1.4x speedup over (sparse convolutional) CenterPoint. This is the first point cloud transformer that achieves real-time performance on edge GPUs and is faster than sparse convolutional methods while achieving on-par or even superior accuracy on large-scale benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_FlatFormer_Flattened_Window_Attention_for_Efficient_Point_Cloud_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_FlatFormer_Flattened_Window_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.08739 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_FlatFormer_Flattened_Window_Attention_for_Efficient_Point_Cloud_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_FlatFormer_Flattened_Window_Attention_for_Efficient_Point_Cloud_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Unbiased Scene Graph Generation in Videos | Sayak Nag, Kyle Min, Subarna Tripathi, Amit K. Roy-Chowdhury | The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and the long-tailed distribution of the visual relationships in addition to the already existing challenges in image-based SGG. Existing methods for dynamic SGG have primarily focused on capturing spatio-temporal context using complex architectures without addressing the challenges mentioned above, especially the long-tailed distribution of relationships. This often leads to the generation of biased scene graphs. To address these challenges, we introduce a new framework called TEMPURA: TEmporal consistency and Memory Prototype guided UnceRtainty Attenuation for unbiased dynamic SGG. TEMPURA employs object-level temporal consistencies via transformer-based sequence modeling, learns to synthesize unbiased relationship representations using memory-guided training, and attenuates the predictive uncertainty of visual relations using a Gaussian Mixture Model (GMM). Extensive experiments demonstrate that our method achieves significant (up to 10% in some cases) performance gain over existing methods highlight- ing its superiority in generating more unbiased scene graphs. Code: https://github.com/sayaknag/unbiasedSGG.git | https://openaccess.thecvf.com/content/CVPR2023/papers/Nag_Unbiased_Scene_Graph_Generation_in_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nag_Unbiased_Scene_Graph_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00733 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nag_Unbiased_Scene_Graph_Generation_in_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nag_Unbiased_Scene_Graph_Generation_in_Videos_CVPR_2023_paper.html | CVPR 2023 | https://openaccess.thecvf.com |
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection | Yuan Wang, Kun Yu, Chen Chen, Xiyuan Hu, Silong Peng | With the springing up of face synthesis techniques, it is prominent in need to develop powerful face forgery detection methods due to security concerns. Some existing methods attempt to employ auxiliary frequency-aware information combined with CNN backbones to discover the forged clues. Due to the inadequate information interaction with image content, the extracted frequency features are thus spatially irrelavant, struggling to generalize well on increasingly realistic counterfeit types. To address this issue, we propose a Spatial-Frequency Dynamic Graph method to exploit the relation-aware features in spatial and frequency domains via dynamic graph learning. To this end, we introduce three well-designed components: 1) Content-guided Adaptive Frequency Extraction module to mine the content-adaptive forged frequency clues. 2) Multiple Domains Attention Map Learning module to enrich the spatial-frequency contextual features with multiscale attention maps. 3) Dynamic Graph Spatial-Frequency Feature Fusion Network to explore the high-order relation of spatial and frequency features. Extensive experiments on several benchmark show that our proposed method sustainedly exceeds the state-of-the-arts by a considerable margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Dynamic_Graph_Learning_With_Content-Guided_Spatial-Frequency_Relation_Reasoning_for_Deepfake_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Dynamic_Graph_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Dynamic_Graph_Learning_With_Content-Guided_Spatial-Frequency_Relation_Reasoning_for_Deepfake_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Dynamic_Graph_Learning_With_Content-Guided_Spatial-Frequency_Relation_Reasoning_for_Deepfake_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images | Ming Y. Lu, Bowen Chen, Andrew Zhang, Drew F. K. Williamson, Richard J. Chen, Tong Ding, Long Phi Le, Yung-Sung Chuang, Faisal Mahmood | Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of image-text pairs and have been designed to perform downstream tasks involving only small to medium sized-images, neither of which are applicable to the emerging field of computational pathology where there are limited publicly available paired image-text datasets and each image can span up to 100,000 x 100,000 pixels in dimensions. In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models to gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels. MI-Zero reformulates zero-shot transfer under the framework of multiple instance learning to overcome the computational challenge of inference on extremely large images. We used over 550k pathology reports and other available in-domain text corpora to pretrain our text encoder. By effectively leveraging strong pretrained encoders, our best model pretrained on over 33k histopathology image-caption pairs achieves an average median zero-shot accuracy of 70.2% across three different real-world cancer subtyping tasks. Our code is available at: https://github.com/mahmoodlab/MI-Zero. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_Visual_Language_Pretrained_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.html | CVPR 2023 | null |
MIST: Multi-Modal Iterative Spatial-Temporal Transformer for Long-Form Video Question Answering | Difei Gao, Luowei Zhou, Lei Ji, Linchao Zhu, Yi Yang, Mike Zheng Shou | To build Video Question Answering (VideoQA) systems capable of assisting humans in daily activities, seeking answers from long-form videos with diverse and complex events is a must. Existing multi-modal VQA models achieve promising performance on images or short video clips, especially with the recent success of large-scale multi-modal pre-training. However, when extending these methods to long-form videos, new challenges arise. On the one hand, using a dense video sampling strategy is computationally prohibitive. On the other hand, methods relying on sparse sampling struggle in scenarios where multi-event and multi-granularity visual reasoning are required. In this work, we introduce a new model named Multi-modal Iterative Spatial-temporal Transformer (MIST) to better adapt pre-trained models for long-form VideoQA. Specifically, MIST decomposes traditional dense spatial-temporal self-attention into cascaded segment and region selection modules that adaptively select frames and image regions that are closely relevant to the question itself. Visual concepts at different granularities are then processed efficiently through an attention module. In addition, MIST iteratively conducts selection and attention over multiple layers to support reasoning over multiple events. The experimental results on four VideoQA datasets, including AGQA, NExT-QA, STAR, and Env-QA, show that MIST achieves state-of-the-art performance and is superior at computation efficiency and interpretability. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_MIST_Multi-Modal_Iterative_Spatial-Temporal_Transformer_for_Long-Form_Video_Question_Answering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_MIST_Multi-Modal_Iterative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09522 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_MIST_Multi-Modal_Iterative_Spatial-Temporal_Transformer_for_Long-Form_Video_Question_Answering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_MIST_Multi-Modal_Iterative_Spatial-Temporal_Transformer_for_Long-Form_Video_Question_Answering_CVPR_2023_paper.html | CVPR 2023 | null |
PMR: Prototypical Modal Rebalance for Multimodal Learning | Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, Song Guo | Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations. However, existing MML methods often optimize a uniform objective for different modalities, leading to the notorious "modality imbalance" problem and counterproductive MML performance. To address the problem, some existing methods modulate the learning pace based on the fused modality, which is dominated by the better modality and eventually results in a limited improvement on the worse modal. To better exploit the features of multimodal, we propose Prototypical Modality Rebalance (PMR) to perform stimulation on the particular slow-learning modality without interference from other modalities. Specifically, we introduce the prototypes that represent general features for each class, to build the non-parametric classifiers for uni-modal performance evaluation. Then, we try to accelerate the slow-learning modality by enhancing its clustering toward prototypes. Furthermore, to alleviate the suppression from the dominant modality, we introduce a prototype-based entropy regularization term during the early training stage to prevent premature convergence. Besides, our method only relies on the representations of each modality and without restrictions from model structures and fusion methods, making it with great application potential for various scenarios. The source code is available here. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_PMR_Prototypical_Modal_Rebalance_for_Multimodal_Learning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.07089 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_PMR_Prototypical_Modal_Rebalance_for_Multimodal_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_PMR_Prototypical_Modal_Rebalance_for_Multimodal_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Two-Stage Co-Segmentation Network Based on Discriminative Representation for Recovering Human Mesh From Videos | Boyang Zhang, Kehua Ma, Suping Wu, Zhixiang Yuan | Recovering 3D human mesh from videos has recently made significant progress. However, most of the existing methods focus on the temporal consistency of videos, while ignoring the spatial representation in complex scenes, thus failing to recover a reasonable and smooth human mesh sequence under extreme illumination and chaotic backgrounds.To alleviate this problem, we propose a two-stage co-segmentation network based on discriminative representation for recovering human body meshes from videos. Specifically, the first stage of the network segments the video spatial domain to spotlight spatially fine-grained information, and then learns and enhances the intra-frame discriminative representation through a dual-excitation mechanism and a frequency domain enhancement module, while suppressing irrelevant information (e.g., background). The second stage focuses on temporal context by segmenting the video temporal domain, and models inter-frame discriminative representation via a dynamic integration strategy.Further, to efficiently generate reasonable human discriminative actions, we carefully elaborate a landmark anchor area loss to constrain the variation of the human motion area. Extensive experimental results on large publicly available datasets indicate the superiority in comparison with most state-of-the-art. Code will be made public. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Two-Stage_Co-Segmentation_Network_Based_on_Discriminative_Representation_for_Recovering_Human_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Two-Stage_Co-Segmentation_Network_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Two-Stage_Co-Segmentation_Network_Based_on_Discriminative_Representation_for_Recovering_Human_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Two-Stage_Co-Segmentation_Network_Based_on_Discriminative_Representation_for_Recovering_Human_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Sensor Large-Scale Dataset for Multi-View 3D Reconstruction | Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Saveliy Galochkin, Andrei-Timotei Ardelean, Arseniy Bozhenko, Ekaterina Karmanova, Pavel Kopanev, Yaroslav Labutin-Rymsho, Ruslan Rakhimov, Aleksandr Safin, Valerii Serpiva, Alexey Artemov, Evgeny Burnaev, Dzmitry Tsetserukou, Denis Zorin | We present a new multi-sensor dataset for multi-view 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and structured-light scanner. The scenes are selected to emphasize a diverse set of material properties challenging for existing algorithms. We provide around 1.4 million images of 107 different scenes acquired from 100 viewing directions under 14 lighting conditions. We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks. The dataset is available at skoltech3d.appliedai.tech. | https://openaccess.thecvf.com/content/CVPR2023/papers/Voynov_Multi-Sensor_Large-Scale_Dataset_for_Multi-View_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Voynov_Multi-Sensor_Large-Scale_Dataset_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2203.06111 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Voynov_Multi-Sensor_Large-Scale_Dataset_for_Multi-View_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Voynov_Multi-Sensor_Large-Scale_Dataset_for_Multi-View_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Privacy-Preserving Representations Are Not Enough: Recovering Scene Content From Camera Poses | Kunal Chelani, Torsten Sattler, Fredrik Kahl, Zuzana Kukelova | Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3D computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloud-based applications, privacy issues are becoming a very important aspect of the localization process. Existing work on privacy-preserving localization aims to defend against an attacker who has access to a cloud-based service. In this paper, we show that an attacker can learn about details of a scene without any access by simply querying a localization service. The attack is based on the observation that modern visual localization algorithms are robust to variations in appearance and geometry. While this is in general a desired property, it also leads to algorithms localizing objects that are similar enough to those present in a scene. An attacker can thus query a server with a large enough set of images of objects, e.g., obtained from the Internet, and some of them will be localized. The attacker can thus learn about object placements from the camera poses returned by the service (which is the minimal information returned by such a service). In this paper, we develop a proof-of-concept version of this attack and demonstrate its practical feasibility. The attack does not place any requirements on the localization algorithm used, and thus also applies to privacy-preserving representations. Current work on privacy-preserving representations alone is thus insufficient. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chelani_Privacy-Preserving_Representations_Are_Not_Enough_Recovering_Scene_Content_From_Camera_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chelani_Privacy-Preserving_Representations_Are_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chelani_Privacy-Preserving_Representations_Are_Not_Enough_Recovering_Scene_Content_From_Camera_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chelani_Privacy-Preserving_Representations_Are_Not_Enough_Recovering_Scene_Content_From_Camera_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Anchor Transformations for 3D Garment Animation | Fang Zhao, Zekun Li, Shaoli Huang, Junwu Weng, Tianfei Zhou, Guo-Sen Xie, Jue Wang, Ying Shan | This paper proposes an anchor-based deformation model, namely AnchorDEF, to predict 3D garment animation from a body motion sequence. It deforms a garment mesh template by a mixture of rigid transformations with extra nonlinear displacements. A set of anchors around the mesh surface is introduced to guide the learning of rigid transformation matrices. Once the anchor transformations are found, per-vertex nonlinear displacements of the garment template can be regressed in a canonical space, which reduces the complexity of deformation space learning. By explicitly constraining the transformed anchors to satisfy the consistencies of position, normal and direction, the physical meaning of learned anchor transformations in space is guaranteed for better generalization. Furthermore, an adaptive anchor updating is proposed to optimize the anchor position by being aware of local mesh topology for learning representative anchor transformations. Qualitative and quantitative experiments on different types of garments demonstrate that AnchorDEF achieves the state-of-the-art performance on 3D garment deformation prediction in motion, especially for loose-fitting garments. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Learning_Anchor_Transformations_for_3D_Garment_Animation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Learning_Anchor_Transformations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Learning_Anchor_Transformations_for_3D_Garment_Animation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Learning_Anchor_Transformations_for_3D_Garment_Animation_CVPR_2023_paper.html | CVPR 2023 | null |
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition | Lilang Lin, Jiahang Zhang, Jiaying Liu | The self-supervised pretraining paradigm has achieved great success in skeleton-based action recognition. However, these methods treat the motion and static parts equally, and lack an adaptive design for different parts, which has a negative impact on the accuracy of action recognition. To realize the adaptive action modeling of both parts, we propose an Actionlet-Dependent Contrastive Learning method (ActCLR). The actionlet, defined as the discriminative subset of the human skeleton, effectively decomposes motion regions for better action modeling. In detail, by contrasting with the static anchor without motion, we extract the motion region of the skeleton data, which serves as the actionlet, in an unsupervised manner. Then, centering on actionlet, a motion-adaptive data transformation method is built. Different data transformations are applied to actionlet and non-actionlet regions to introduce more diversity while maintaining their own characteristics. Meanwhile, we propose a semantic-aware feature pooling method to build feature representations among motion and static regions in a distinguished manner. Extensive experiments on NTU RGB+D and PKUMMD show that the proposed method achieves remarkable action recognition performance. More visualization and quantitative experiments demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Actionlet-Dependent_Contrastive_Learning_for_Unsupervised_Skeleton-Based_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Actionlet-Dependent_Contrastive_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10904 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Actionlet-Dependent_Contrastive_Learning_for_Unsupervised_Skeleton-Based_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Actionlet-Dependent_Contrastive_Learning_for_Unsupervised_Skeleton-Based_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization | Yuechen Zhang, Zexin He, Jinbo Xing, Xufeng Yao, Jiaya Jia | Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Based Non-Photorealistic Radiance Fields (Ref-NPR) to address this limitation. This controllable method stylizes a 3D scene using radiance fields with a single stylized 2D view as a reference. We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views. Then we exploit semantic correspondences in content images to fill occluded regions with perceptually similar styles, resulting in non-photorealistic and continuous novel view sequences. Our experimental results demonstrate that Ref-NPR outperforms existing scene and video stylization methods regarding visual quality and semantic correspondence. The code and data are publicly available on the project page at https://ref-npr.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Ref-NPR_Reference-Based_Non-Photorealistic_Radiance_Fields_for_Controllable_Scene_Stylization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Ref-NPR_Reference-Based_Non-Photorealistic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Ref-NPR_Reference-Based_Non-Photorealistic_Radiance_Fields_for_Controllable_Scene_Stylization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Ref-NPR_Reference-Based_Non-Photorealistic_Radiance_Fields_for_Controllable_Scene_Stylization_CVPR_2023_paper.html | CVPR 2023 | null |
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360deg | Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Y. Ogras, Linjie Luo | Synthesis and reconstruction of 3D human head has gained increasing interests in computer vision and computer graphics recently. Existing state-of-the-art 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360deg with diverse appearance and detailed geometry using only in-the-wild unstructured images for training. At its core, we lift up the representation power of recent 3D GANs and bridge the data alignment gap when training from in-the-wild images with widely distributed views. Specifically, we propose a novel two-stage self-adaptive image alignment for robust 3D GAN training. We further introduce a tri-grid neural volume representation that effectively addresses front-face and back-head feature entanglement rooted in the widely-adopted tri-plane formulation. Our method instills prior knowledge of 2D image segmentation in adversarial learning of 3D neural scene structures, enabling compositable head synthesis in diverse backgrounds. Benefiting from these designs, our method significantly outperforms previous 3D GANs, generating high-quality 3D heads with accurate geometry and diverse appearances, even with long wavy and afro hairstyles, renderable from arbitrary poses. Furthermore, we show that our system can reconstruct full 3D heads from single input images for personalized realistic 3D avatars. | https://openaccess.thecvf.com/content/CVPR2023/papers/An_PanoHead_Geometry-Aware_3D_Full-Head_Synthesis_in_360deg_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/An_PanoHead_Geometry-Aware_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/An_PanoHead_Geometry-Aware_3D_Full-Head_Synthesis_in_360deg_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/An_PanoHead_Geometry-Aware_3D_Full-Head_Synthesis_in_360deg_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Feature-Based Knowledge Distillation for Face Recognition | Jingzhi Li, Zidong Guo, Hui Li, Seungju Han, Ji-won Baek, Min Yang, Ran Yang, Sungjoo Suh | With the continual expansion of face datasets, feature-based distillation prevails for large-scale face recognition. In this work, we attempt to remove identity supervision in student training, to spare the GPU memory from saving massive class centers. However, this naive removal leads to inferior distillation result. We carefully inspect the performance degradation from the perspective of intrinsic dimension, and argue that the gap in intrinsic dimension, namely the intrinsic gap, is intimately connected to the infamous capacity gap problem. By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation. Remarkably, the proposed reverse distillation creates universally student-friendly teacher that demonstrates outstanding student improvement. We further enhance its effectiveness by designing a student proxy to better bridge the intrinsic gap. As a result, the proposed method surpasses state-of-the-art distillation techniques with identity supervision on various face recognition benchmarks, and the improvements are consistent across different teacher-student pairs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Rethinking_Feature-Based_Knowledge_Distillation_for_Face_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Rethinking_Feature-Based_Knowledge_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Rethinking_Feature-Based_Knowledge_Distillation_for_Face_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Rethinking_Feature-Based_Knowledge_Distillation_for_Face_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization | Zhixiang Min, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Enrique Dunn, Manmohan Chandraker | Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature. Estimating 3D coordinates for each pixel on the object surface holds great potential as it provides dense 2D-3D geometric constraints for the underlying PnP problem. However, high-quality ground truth supervision is not available in driving scenes due to sparsity and various artifacts of Lidar data, as well as the practical infeasibility of collecting per-instance CAD models. In this work, we present NeurOCS, a framework that uses instance masks and 3D boxes as input to learn 3D object shapes by means of differentiable rendering, which further serves as supervision for learning dense object coordinates. Our approach rests on insights in learning a category-level shape prior directly from real driving scenes, while properly handling single-view ambiguities. Furthermore, we study and make critical design choices to learn object coordinates more effectively from an object-centric view. Altogether, our framework leads to new state-of-the-art in monocular 3D localization that ranks 1st on the KITTI-Object benchmark among published monocular methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Min_NeurOCS_Neural_NOCS_Supervision_for_Monocular_3D_Object_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Min_NeurOCS_Neural_NOCS_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Min_NeurOCS_Neural_NOCS_Supervision_for_Monocular_3D_Object_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Min_NeurOCS_Neural_NOCS_Supervision_for_Monocular_3D_Object_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Tree Instance Segmentation With Temporal Contour Graph | Adnan Firoze, Cameron Wingren, Raymond A. Yeh, Bedrich Benes, Daniel Aliaga | We present a novel approach to perform instance segmentation, and counting, for densely packed self-similar trees using a top-view RGB image sequence. We propose a solution that leverages pixel content, shape, and self-occlusion. First, we perform an initial over-segmentation of the image sequence and aggregate structural characteristics into a contour graph with temporal information incorporated. Second, using a graph convolutional network and its inherent local messaging passing abilities, we merge adjacent tree crown patches into a final set of tree crowns. Per various studies and comparisons, our method is superior to all prior methods and results in high-accuracy instance segmentation and counting, despite the trees being tightly packed. Finally, we provide various forest image sequence datasets suitable for subsequent benchmarking and evaluation captured at different altitudes and leaf conditions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Firoze_Tree_Instance_Segmentation_With_Temporal_Contour_Graph_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Firoze_Tree_Instance_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Firoze_Tree_Instance_Segmentation_With_Temporal_Contour_Graph_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Firoze_Tree_Instance_Segmentation_With_Temporal_Contour_Graph_CVPR_2023_paper.html | CVPR 2023 | null |
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet Categories | Reza Akbarian Bafghi, Danna Gurari | Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain. Complementing existing work in robustness testing, we introduce the first dataset for this purpose which comes from an authentic use case where photographers wanted to learn about the content in their images. We built a new test set using 8,900 images taken by people who are blind for which we collected metadata to indicate the presence versus absence of 200 ImageNet object categories. We call this dataset VizWiz-Classification. We characterize this dataset and how it compares to the mainstream datasets for evaluating how well ImageNet-trained classification models generalize. Finally, we analyze the performance of 100 ImageNet classification models on our new test dataset. Our fine-grained analysis demonstrates that these models struggle on images with quality issues. To enable future extensions to this work, we share our new dataset with evaluation server at: https://vizwiz.org/tasks-and-datasets/image-classification | https://openaccess.thecvf.com/content/CVPR2023/papers/Bafghi_A_New_Dataset_Based_on_Images_Taken_by_Blind_People_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bafghi_A_New_Dataset_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bafghi_A_New_Dataset_Based_on_Images_Taken_by_Blind_People_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bafghi_A_New_Dataset_Based_on_Images_Taken_by_Blind_People_CVPR_2023_paper.html | CVPR 2023 | null |
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency | Xiaogeng Liu, Minghui Li, Haoyu Wang, Shengshan Hu, Dengpan Ye, Hai Jin, Libing Wu, Chaowei Xiao | Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo), a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. The code is available at https://github.com/CGCL-codes/TeCo | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Detecting_Backdoors_During_the_Inference_Stage_Based_on_Corruption_Robustness_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Detecting_Backdoors_During_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18191 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Detecting_Backdoors_During_the_Inference_Stage_Based_on_Corruption_Robustness_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Detecting_Backdoors_During_the_Inference_Stage_Based_on_Corruption_Robustness_CVPR_2023_paper.html | CVPR 2023 | null |
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation | Phoenix Neale Williams, Ke Li | Deep neural networks (DNNs) are susceptible to adversarial images, raising concerns about their reliability in safety-critical tasks. Sparse adversarial attacks, which limit the number of modified pixels, have shown to be highly effective in causing DNNs to misclassify. However, existing methods often struggle to simultaneously minimize the number of modified pixels and the size of the modifications, often requiring a large number of queries and assuming unrestricted access to the targeted DNN. In contrast, other methods that limit the number of modified pixels often permit unbounded modifications, making them easily detectable. To address these limitations, we propose a novel multi-objective sparse attack algorithm that efficiently minimizes the number of modified pixels and their size during the attack process. Our algorithm draws inspiration from evolutionary computation and incorporates a mechanism for prioritizing objectives that aligns with an attacker's goals. Our approach outperforms existing sparse attacks on CIFAR-10 and ImageNet trained DNN classifiers while requiring only a small query budget, attaining competitive attack success rates while perturbing fewer pixels. Overall, our proposed attack algorithm provides a solution to the limitations of current sparse attack methods by jointly minimizing the number of modified pixels and their size. Our results demonstrate the effectiveness of our approach in restricted scenarios, highlighting its potential to enhance DNN security. | https://openaccess.thecvf.com/content/CVPR2023/papers/Williams_Black-Box_Sparse_Adversarial_Attack_via_Multi-Objective_Optimisation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Williams_Black-Box_Sparse_Adversarial_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Williams_Black-Box_Sparse_Adversarial_Attack_via_Multi-Objective_Optimisation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Williams_Black-Box_Sparse_Adversarial_Attack_via_Multi-Objective_Optimisation_CVPR_2023_paper.html | CVPR 2023 | null |
Renderable Neural Radiance Map for Visual Navigation | Obin Kwon, Jeongho Park, Songhwai Oh | We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment. The RNR-Map has a grid form and consists of latent codes at each pixel. These latent codes are embedded from image observations, and can be converted to the neural radiance field which enables image rendering given a camera pose. The recorded latent codes implicitly contain visual information about the environment, which makes the RNR-Map visually descriptive. This visual information in RNR-Map can be a useful guideline for visual localization and navigation. We develop localization and navigation frameworks that can effectively utilize the RNR-Map. We evaluate the proposed frameworks on camera tracking, visual localization, and image-goal navigation. Experimental results show that the RNR-Map-based localization framework can find the target location based on a single query image with fast speed and competitive accuracy compared to other baselines. Also, this localization framework is robust to environmental changes, and even finds the most visually similar places when a query image from a different environment is given. The proposed navigation framework outperforms the existing image-goal navigation methods in difficult scenarios, under odometry and actuation noises. The navigation framework shows 65.7% success rate in curved scenarios of the NRNS dataset, which is an improvement of 18.6% over the current state-of-the-art. Project page: https://rllab-snu.github.io/projects/RNR-Map/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Kwon_Renderable_Neural_Radiance_Map_for_Visual_Navigation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kwon_Renderable_Neural_Radiance_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.00304 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kwon_Renderable_Neural_Radiance_Map_for_Visual_Navigation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kwon_Renderable_Neural_Radiance_Map_for_Visual_Navigation_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Reverse Distillation for Anomaly Detection | Tran Dinh Tien, Anh Tuan Nguyen, Nguyen Hoang Tran, Ta Duc Huy, Soan T.M. Duong, Chanh D. Tr. Nguyen, Steven Q. H. Truong | Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off. Memory based approaches with dominant performances like PatchCore or Coupled-hypersphere-based Feature Adaptation (CFA) require an external memory bank, which significantly lengthens the execution time. Another approach that employs Reversed Distillation (RD) can perform well while maintaining low latency. In this paper, we revisit this idea to improve its performance, establishing a new state-of-the-art benchmark on the challenging MVTec dataset for both anomaly detection and localization. The proposed method, called RD++, runs six times faster than PatchCore, and two times faster than CFA but introduces a negligible latency compared to RD. We also experiment on the BTAD and Retinal OCT datasets to demonstrate our method's generalizability and conduct important ablation experiments to provide insights into its configurations. Source code will be available at https://github.com/tientrandinh/Revisiting-Reverse-Distillation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tien_Revisiting_Reverse_Distillation_for_Anomaly_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tien_Revisiting_Reverse_Distillation_for_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tien_Revisiting_Reverse_Distillation_for_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Diffusion-Based Generation, Optimization, and Planning in 3D Scenes | Siyuan Huang, Zan Wang, Puhao Li, Baoxiong Jia, Tengyu Liu, Yixin Zhu, Wei Liang, Song-Chun Zhu | We introduce SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning. In contrast to prior works, SceneDiffuser is intrinsically scene-aware, physics-based, and goal-oriented. With an iterative sampling strategy, SceneDiffuser jointly formulates the scene-aware generation, physics-based optimization, and goal-oriented planning via a diffusion-based denoising process in a fully differentiable fashion. Such a design alleviates the discrepancies among different modules and the posterior collapse of previous scene-conditioned generative models. We evaluate SceneDiffuser with various 3D scene understanding tasks, including human pose and motion generation, dexterous grasp generation, path planning for 3D navigation, and motion planning for robot arms. The results show significant improvements compared with previous models, demonstrating the tremendous potential of SceneDiffuser for the broad community of 3D scene understanding. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Diffusion-Based_Generation_Optimization_and_Planning_in_3D_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Diffusion-Based_Generation_Optimization_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2301.06015 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Diffusion-Based_Generation_Optimization_and_Planning_in_3D_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Diffusion-Based_Generation_Optimization_and_Planning_in_3D_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
TMO: Textured Mesh Acquisition of Objects With a Mobile Device by Using Differentiable Rendering | Jaehoon Choi, Dongki Jung, Taejae Lee, Sangwook Kim, Youngdong Jung, Dinesh Manocha, Donghwan Lee | We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps and refines camera poses guided by corresponding depth. Then, we adopt the neural implicit surface reconstruction method, which allows for high quality mesh and develops a new training process for applying a regularization provided by classical multi-view stereo methods. Moreover, we apply a differentiable rendering to fine-tune incomplete texture maps and generate textures which are perceptually closer to the original scene. Our pipeline can be applied to any common objects in the real world without the need for either in-the-lab environments or accurate mask images. We demonstrate results of captured objects with complex shapes and validate our method numerically against existing 3D reconstruction and texture mapping methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_TMO_Textured_Mesh_Acquisition_of_Objects_With_a_Mobile_Device_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_TMO_Textured_Mesh_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15060 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_TMO_Textured_Mesh_Acquisition_of_Objects_With_a_Mobile_Device_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_TMO_Textured_Mesh_Acquisition_of_Objects_With_a_Mobile_Device_CVPR_2023_paper.html | CVPR 2023 | null |
Meta-Causal Learning for Single Domain Generalization | Jin Chen, Zhi Gao, Xinxiao Wu, Jiebo Luo | Single domain generalization aims to learn a model from a single training domain (source domain) and apply it to multiple unseen test domains (target domains). Existing methods focus on expanding the distribution of the training domain to cover the target domains, but without estimating the domain shift between the source and target domains. In this paper, we propose a new learning paradigm, namely simulate-analyze-reduce, which first simulates the domain shift by building an auxiliary domain as the target domain, then learns to analyze the causes of domain shift, and finally learns to reduce the domain shift for model adaptation. Under this paradigm, we propose a meta-causal learning method to learn meta-knowledge, that is, how to infer the causes of domain shift between the auxiliary and source domains during training. We use the meta-knowledge to analyze the shift between the target and source domains during testing. Specifically, we perform multiple transformations on source data to generate the auxiliary domain, perform counterfactual inference to learn to discover the causal factors of the shift between the auxiliary and source domains, and incorporate the inferred causality into factor-aware domain alignments. Extensive experiments on several benchmarks of image classification show the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Meta-Causal_Learning_for_Single_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Meta-Causal_Learning_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03709 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Meta-Causal_Learning_for_Single_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Meta-Causal_Learning_for_Single_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent With Learned Distance Functions | Yun He, Danhang Tang, Yinda Zhang, Xiangyang Xue, Yanwei Fu | Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction. However, they usually suffer from two critical issues: (1) fixed upsampling rate after one-time training, since the feature expansion unit is customized for each upsampling rate; (2) outliers or shrinkage artifact caused by the difficulty of precisely predicting 3D coordinates or residuals of upsampled points. To adress them, we propose a new framework for accurate point cloud upsampling that supports arbitrary upsampling rates. Our method first interpolates the low-res point cloud according to a given upsampling rate. And then refine the positions of the interpolated points with an iterative optimization process, guided by a trained model estimating the difference between the current point cloud and the high-res target. Extensive quantitative and qualitative results on benchmarks and downstream tasks demonstrate that our method achieves the state-of-the-art accuracy and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Grad-PU_Arbitrary-Scale_Point_Cloud_Upsampling_via_Gradient_Descent_With_Learned_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Grad-PU_Arbitrary-Scale_Point_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Grad-PU_Arbitrary-Scale_Point_Cloud_Upsampling_via_Gradient_Descent_With_Learned_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Grad-PU_Arbitrary-Scale_Point_Cloud_Upsampling_via_Gradient_Descent_With_Learned_CVPR_2023_paper.html | CVPR 2023 | null |
Trainable Projected Gradient Method for Robust Fine-Tuning | Junjiao Tian, Zecheng He, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Zsolt Kira | Recent studies on transfer learning have shown that selectively fine-tuning a subset of layers or customizing different learning rates for each layer can greatly improve robustness to out-of-distribution (OOD) data and retain generalization capability in the pre-trained models. However, most of these methods employ manually crafted heuristics or expensive hyper-parameter search, which prevent them from scaling up to large datasets and neural networks. To solve this problem, we propose Trainable Projected Gradient Method (TPGM) to automatically learn the constraint imposed for each layer for a fine-grained fine-tuning regularization. This is motivated by formulating fine-tuning as a bi-level constrained optimization problem. Specifically, TPGM maintains a set of projection radii, i.e., distance constraints between the fine-tuned model and the pre-trained model, for each layer, and enforces them through weight projections. To learn the constraints, we propose a bi-level optimization to automatically learn the best set of projection radii in an end-to-end manner. Theoretically, we show that the bi-level optimization formulation is the key to learn different constraints for each layer. Empirically, with little hyper-parameter search cost, TPGM outperforms existing fine-tuning methods in OOD performance while matching the best in-distribution (ID) performance. For example, when fine-tuned on DomainNet-Real and ImageNet, compared to vanilla fine-tuning, TPGM shows 22% and 10% relative OOD improvement respectively on their sketch counterparts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_Trainable_Projected_Gradient_Method_for_Robust_Fine-Tuning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_Trainable_Projected_Gradient_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10720 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Trainable_Projected_Gradient_Method_for_Robust_Fine-Tuning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Trainable_Projected_Gradient_Method_for_Robust_Fine-Tuning_CVPR_2023_paper.html | CVPR 2023 | null |
Text2Scene: Text-Driven Indoor Scene Stylization With Part-Aware Details | Inwoo Hwang, Hyeonwoo Kim, Young Min Kim | We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labeled 3D geometries in the room such that the generated colors respect the hierarchical structure or semantic parts that are often composed of similar materials. Instead of applying flat stylization on the entire scene at a single step, we obtain weak semantic cues from geometric segmentation, which are further clarified by assigning initial colors to segmented parts. Then we add texture details for individual objects such that their projections on image space exhibit feature embedding aligned with the embedding of the input. The decomposition makes the entire pipeline tractable to a moderate amount of computation resources and memory. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated datasets with high-quality textures designed by skillful artists. To the best of our knowledge, it is the first practical and scalable approach that can create detailed and realistic textures of the desired style that maintain structural context for scenes with multiple objects. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hwang_Text2Scene_Text-Driven_Indoor_Scene_Stylization_With_Part-Aware_Details_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hwang_Text2Scene_Text-Driven_Indoor_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hwang_Text2Scene_Text-Driven_Indoor_Scene_Stylization_With_Part-Aware_Details_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hwang_Text2Scene_Text-Driven_Indoor_Scene_Stylization_With_Part-Aware_Details_CVPR_2023_paper.html | CVPR 2023 | null |
FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-Tail Trajectory Prediction | Yuning Wang, Pu Zhang, Lei Bai, Jianru Xue | Predicting the future trajectories of the traffic agents is a gordian technique in autonomous driving. However, trajectory prediction suffers from data imbalance in the prevalent datasets, and the tailed data is often more complicated and safety-critical. In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction. Previous methods dealing with long-tail data did not take into account the variety of motion patterns in the tailed data. In this paper, we put forward a future enhanced contrastive learning framework to recognize tail trajectory patterns and form a feature space with separate pattern clusters.Furthermore, a distribution aware hyper predictor is brought up to better utilize the shaped feature space.Our method is a model-agnostic framework and can be plugged into many well-known baselines. Experimental results show that our framework outperforms the state-of-the-art long-tail prediction method on tailed samples by 9.5% on ADE and 8.5% on FDE, while maintaining or slightly improving the averaged performance. Our method also surpasses many long-tail techniques on trajectory prediction task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_FEND_A_Future_Enhanced_Distribution-Aware_Contrastive_Learning_Framework_for_Long-Tail_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_FEND_A_Future_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16574 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FEND_A_Future_Enhanced_Distribution-Aware_Contrastive_Learning_Framework_for_Long-Tail_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FEND_A_Future_Enhanced_Distribution-Aware_Contrastive_Learning_Framework_for_Long-Tail_CVPR_2023_paper.html | CVPR 2023 | null |
MP-Former: Mask-Piloted Transformer for Image Segmentation | Hao Zhang, Feng Li, Huaizhe Xu, Shijia Huang, Shilong Liu, Lionel M. Ni, Lei Zhang | We present a mask-piloted Transformer which improves masked-attention in Mask2Former for image segmentation. The improvement is based on our observation that Mask2Former suffers from inconsistent mask predictions between consecutive decoder layers, which leads to inconsistent optimization goals and low utilization of decoder queries. To address this problem, we propose a mask-piloted training approach, which additionally feeds noised ground-truth masks in masked-attention and trains the model to reconstruct the original ones. Compared with the predicted masks used in mask-attention, the ground-truth masks serve as a pilot and effectively alleviate the negative impact of inaccurate mask predictions in Mask2Former. Based on this technique, our MP-Former achieves a remarkable performance improvement on all three image segmentation tasks (instance, panoptic, and semantic), yielding +2.3 AP and +1.6 mIoU on the Cityscapes instance and semantic segmentation tasks with a ResNet-50 backbone. Our method also significantly speeds up the training, outperforming Mask2Former with half of the number of training epochs on ADE20K with both a ResNet-50 and a Swin-L backbones. Moreover, our method only introduces little computation during training and no extra computation during inference. Our code will be released at https://github.com/IDEA-Research/MP-Former. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_MP-Former_Mask-Piloted_Transformer_for_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_MP-Former_Mask-Piloted_Transformer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MP-Former_Mask-Piloted_Transformer_for_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MP-Former_Mask-Piloted_Transformer_for_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
HDR Imaging With Spatially Varying Signal-to-Noise Ratios | Yiheng Chi, Xingguang Zhang, Stanley H. Chan | While today's high dynamic range (HDR) image fusion algorithms are capable of blending multiple exposures, the acquisition is often controlled so that the dynamic range within one exposure is narrow. For HDR imaging in photon-limited situations, the dynamic range can be enormous and the noise within one exposure is spatially varying. Existing image denoising algorithms and HDR fusion algorithms both fail to handle this situation, leading to severe limitations in low-light HDR imaging. This paper presents two contributions. Firstly, we identify the source of the problem. We find that the issue is associated with the co-existence of (1) spatially varying signal-to-noise ratio, especially the excessive noise due to very dark regions, and (2) a wide luminance range within each exposure. We show that while the issue can be handled by a bank of denoisers, the complexity is high. Secondly, we propose a new method called the spatially varying high dynamic range (SV-HDR) fusion network to simultaneously denoise and fuse images. We introduce a new exposure-shared block within our custom-designed multi-scale transformer framework. In a variety of testing conditions, the performance of the proposed SV-HDR is better than the existing methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chi_HDR_Imaging_With_Spatially_Varying_Signal-to-Noise_Ratios_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.17253 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chi_HDR_Imaging_With_Spatially_Varying_Signal-to-Noise_Ratios_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chi_HDR_Imaging_With_Spatially_Varying_Signal-to-Noise_Ratios_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation | Sun-Ao Liu, Yiheng Zhang, Zhaofan Qiu, Hongtao Xie, Yongdong Zhang, Ting Yao | Generalized few-shot semantic segmentation (GFSS) distinguishes pixels of base and novel classes from the background simultaneously, conditioning on sufficient data of base classes and a few examples from novel class. A typical GFSS approach has two training phases: base class learning and novel class updating. Nevertheless, such a stand-alone updating process often compromises the well-learnt features and results in performance drop on base classes. In this paper, we propose a new idea of leveraging Projection onto Orthogonal Prototypes (POP), which updates features to identify novel classes without compromising base classes. POP builds a set of orthogonal prototypes, each of which represents a semantic class, and makes the prediction for each class separately based on the features projected onto its prototype. Technically, POP first learns prototypes on base data, and then extends the prototype set to novel classes. The orthogonal constraint of POP encourages the orthogonality between the learnt prototypes and thus mitigates the influence on base class features when generalizing to novel prototypes. Moreover, we capitalize on the residual of feature projection as the background representation to dynamically fit semantic shifting (i.e., background no longer includes the pixels of novel classes in updating phase). Extensive experiments on two benchmarks demonstrate that our POP achieves superior performances on novel classes without sacrificing much accuracy on base classes. Notably, POP outperforms the state-of-the-art fine-tuning by 3.93% overall mIoU on PASCAL-5i in 5-shot scenario. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Learning_Orthogonal_Prototypes_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learning_Orthogonal_Prototypes_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learning_Orthogonal_Prototypes_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
TAPS3D: Text-Guided 3D Textured Shape Generation From Pseudo Supervision | Jiacheng Wei, Hao Wang, Jiashi Feng, Guosheng Lin, Kim-Hui Yap | In this paper, we investigate an open research task of generating controllable 3D textured shapes from the given textual descriptions. Previous works either require ground truth caption labeling or extensive optimization time. To resolve these issues, we present a novel framework, TAPS3D, to train a text-guided 3D shape generator with pseudo captions. Specifically, based on rendered 2D images, we retrieve relevant words from the CLIP vocabulary and construct pseudo captions using templates. Our constructed captions provide high-level semantic supervision for generated 3D shapes. Further, in order to produce fine-grained textures and increase geometry diversity, we propose to adopt low-level image regularization to enable fake-rendered images to align with the real ones. During the inference phase, our proposed model can generate 3D textured shapes from the given text without any additional optimization. We conduct extensive experiments to analyze each of our proposed components and show the efficacy of our framework in generating high-fidelity 3D textured and text-relevant shapes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_TAPS3D_Text-Guided_3D_Textured_Shape_Generation_From_Pseudo_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_TAPS3D_Text-Guided_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13273 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_TAPS3D_Text-Guided_3D_Textured_Shape_Generation_From_Pseudo_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_TAPS3D_Text-Guided_3D_Textured_Shape_Generation_From_Pseudo_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Are Deep Neural Networks SMARTer Than Second Graders? | Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Kevin A. Smith, Joshua B. Tenenbaum | Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, question answering (such as ChatGPT), etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6--8 age group. Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning, among others. To scale our dataset towards training deep neural networks, we programmatically generate entirely new instances for each puzzle while retaining their solution algorithm. To benchmark the performance on the SMART-101 dataset, we propose a vision-and-language meta-learning model that can incorporate varied state-of-the-art neural backbones. Our experiments reveal that while powerful deep models offer reasonable performances on puzzles in a supervised setting, they are not better than random accuracy when analyzed for generalization -- filling this gap may demand new multimodal learning approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cherian_Are_Deep_Neural_Networks_SMARTer_Than_Second_Graders_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.09993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cherian_Are_Deep_Neural_Networks_SMARTer_Than_Second_Graders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cherian_Are_Deep_Neural_Networks_SMARTer_Than_Second_Graders_CVPR_2023_paper.html | CVPR 2023 | null |
Reliability in Semantic Segmentation: Are We on the Right Track? | Pau de Jorge, Riccardo Volpi, Philip H.S. Torr, Grégory Rogez | Motivated by the increasing popularity of transformers in computer vision, in recent times there has been a rapid development of novel architectures. While in-domain performance follows a constant, upward trend, properties like robustness or uncertainty estimation are less explored -leaving doubts about advances in model reliability. Studies along these axes exist, but they are mainly limited to classification models. In contrast, we carry out a study on semantic segmentation, a relevant task for many real-world applications where model reliability is paramount. We analyze a broad variety of models, spanning from older ResNet-based architectures to novel transformers and assess their reliability based on four metrics: robustness, calibration, misclassification detection and out-of-distribution (OOD) detection. We find that while recent models are significantly more robust, they are not overall more reliable in terms of uncertainty estimation. We further explore methods that can come to the rescue and show that improving calibration can also help with other uncertainty metrics such as misclassification or OOD detection. This is the first study on modern segmentation models focused on both robustness and uncertainty estimation and we hope it will help practitioners and researchers interested in this fundamental vision task. | https://openaccess.thecvf.com/content/CVPR2023/papers/de_Jorge_Reliability_in_Semantic_Segmentation_Are_We_on_the_Right_Track_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/de_Jorge_Reliability_in_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11298 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/de_Jorge_Reliability_in_Semantic_Segmentation_Are_We_on_the_Right_Track_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/de_Jorge_Reliability_in_Semantic_Segmentation_Are_We_on_the_Right_Track_CVPR_2023_paper.html | CVPR 2023 | null |
Video Test-Time Adaptation for Action Recognition | Wei Lin, Muhammad Jehanzeb Mirza, Mateusz Kozinski, Horst Possegger, Hilde Kuehne, Horst Bischof | Although action recognition systems can achieve top performance when evaluated on in-distribution test points, they are vulnerable to unanticipated distribution shifts in test data. However, test-time adaptation of video action recognition models against common distribution shifts has so far not been demonstrated. We propose to address this problem with an approach tailored to spatio-temporal models that is capable of adaptation on a single video sample at a step. It consists in a feature distribution alignment technique that aligns online estimates of test set statistics towards the training statistics. We further enforce prediction consistency over temporally augmented views of the same test video sample. Evaluations on three benchmark action recognition datasets show that our proposed technique is architecture-agnostic and able to significantly boost the performance on both, the state of the art convolutional architecture TANet and the Video Swin Transformer. Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Video_Test-Time_Adaptation_for_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Video_Test-Time_Adaptation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15393 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Video_Test-Time_Adaptation_for_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Video_Test-Time_Adaptation_for_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Bi-Level Meta-Learning for Few-Shot Domain Generalization | Xiaorong Qin, Xinhang Song, Shuqiang Jiang | The goal of few-shot learning is to learn the generalizability from seen to unseen data with only a few samples. Most previous few-shot learning focus on learning generalizability within particular domains. However, the more practical scenarios may also require generalizability across domains. In this paper, we study the problem of Few-shot domain generalization (FSDG), which is a more challenging variant of few-shot classification. FSDG requires additional generalization with larger gap from seen domains to unseen domains. We address FSDG problem by meta-learning two levels of meta-knowledge, where the lower-level meta-knowledge are domain-specific embedding spaces as subspaces of a base space for intra-domain generalization, and the upper-level meta-knowledge is the base space and a prior subspace over domain-specific spaces for inter-domain generalization. We formulate the two levels of meta-knowledge learning problem with bi-level optimization, and further develop an optimization algorithm without Hessian information to solve it. We demonstrate our method is significantly superior to the previous works by evaluating it on the widely used benchmark Meta-Dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Bi-Level_Meta-Learning_for_Few-Shot_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_Bi-Level_Meta-Learning_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Bi-Level_Meta-Learning_for_Few-Shot_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Bi-Level_Meta-Learning_for_Few-Shot_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Tensor4D: Efficient Neural 4D Decomposition for High-Fidelity Dynamic Reconstruction and Rendering | Ruizhi Shao, Zerong Zheng, Hanzhang Tu, Boning Liu, Hongwen Zhang, Yebin Liu | We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor. To tackle the accompanying memory issue, we decompose the 4D tensor hierarchically by projecting it first into three time-aware volumes and then nine compact feature planes. In this way, spatial information over time can be simultaneously captured in a compact and memory-efficient manner. When applying Tensor4D for dynamic scene reconstruction and rendering, we further factorize the 4D fields to different scales in the sense that structural motions and dynamic detailed changes can be learned from coarse to fine. The effectiveness of our method is validated on both synthetic and real-world scenes. Extensive experiments show that our method is able to achieve high-quality dynamic reconstruction and rendering from sparse-view camera rigs or even a monocular camera. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shao_Tensor4D_Efficient_Neural_4D_Decomposition_for_High-Fidelity_Dynamic_Reconstruction_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shao_Tensor4D_Efficient_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Tensor4D_Efficient_Neural_4D_Decomposition_for_High-Fidelity_Dynamic_Reconstruction_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Tensor4D_Efficient_Neural_4D_Decomposition_for_High-Fidelity_Dynamic_Reconstruction_and_CVPR_2023_paper.html | CVPR 2023 | null |
Blowing in the Wind: CycleNet for Human Cinemagraphs From Still Images | Hugo Bertiche, Niloy J. Mitra, Kuldeep Kulkarni, Chun-Hao P. Huang, Tuanfeng Y. Wang, Meysam Madadi, Sergio Escalera, Duygu Ceylan | Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we present an automatic method that allows generating human cinemagraphs from single RGB images. We investigate the problem in the context of dressed humans under the wind. At the core of our method is a novel cyclic neural network that produces looping cinemagraphs for the target loop duration. To circumvent the problem of collecting real data, we demonstrate that it is possible, by working in the image normal space, to learn garment motion dynamics on synthetic data and generalize to real data. We evaluate our method on both synthetic and real data and demonstrate that it is possible to create compelling and plausible cinemagraphs from single RGB images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bertiche_Blowing_in_the_Wind_CycleNet_for_Human_Cinemagraphs_From_Still_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08639 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bertiche_Blowing_in_the_Wind_CycleNet_for_Human_Cinemagraphs_From_Still_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bertiche_Blowing_in_the_Wind_CycleNet_for_Human_Cinemagraphs_From_Still_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos | Ziqian Bai, Feitong Tan, Zeng Huang, Kripasindhu Sarkar, Danhang Tang, Di Qiu, Abhimitra Meka, Ruofei Du, Mingsong Dou, Sergio Orts-Escolano, Rohit Pandey, Ping Tan, Thabo Beeler, Sean Fanello, Yinda Zhang | We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism. To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry. These learnt features are driven by 3DMM deformation and interpolated in 3D space to yield the volumetric radiance at a designated query point. We further show that using a Convolutional Neural Network in the UV space is critical in incorporating spatial context and producing representative local features. Extensive experiments show that we are able to reconstruct high-quality avatars, with more accurate expression-dependent details, good generalization to out-of-training expressions, and quantitatively superior renderings compared to other state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_Learning_Personalized_High_Quality_Volumetric_Head_Avatars_From_Monocular_RGB_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_Learning_Personalized_High_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.01436 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Learning_Personalized_High_Quality_Volumetric_Head_Avatars_From_Monocular_RGB_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Learning_Personalized_High_Quality_Volumetric_Head_Avatars_From_Monocular_RGB_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Modal Learning With Missing Modality via Shared-Specific Feature Modelling | Hu Wang, Yuanhong Chen, Congbo Ma, Jodie Avery, Louise Hull, Gustavo Carneiro | The missing modality issue is critical but non-trivial to be solved by multi-modal models. Current methods aiming to handle the missing modality problem in multi-modal tasks, either deal with missing modalities only during evaluation or train separate models to handle specific missing modality settings. In addition, these models are designed for specific tasks, so for example, classification models are not easily adapted to segmentation tasks and vice versa. In this paper, we propose the Shared-Specific Feature Modelling (ShaSpec) method that is considerably simpler and more effective than competing approaches that address the issues above. ShaSpec is designed to take advantage of all available input modalities during training and evaluation by learning shared and specific features to better represent the input data. This is achieved from a strategy that relies on auxiliary tasks based on distribution alignment and domain classification, in addition to a residual feature fusion procedure. Also, the design simplicity of ShaSpec enables its easy adaptation to multiple tasks, such as classification and segmentation. Experiments are conducted on both medical image segmentation and computer vision classification, with results indicating that ShaSpec outperforms competing methods by a large margin. For instance, on BraTS2018, ShaSpec improves the SOTA by more than 3% for enhancing tumour, 5% for tumour core and 3% for whole tumour. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Multi-Modal_Learning_With_Missing_Modality_via_Shared-Specific_Feature_Modelling_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multi-Modal_Learning_With_Missing_Modality_via_Shared-Specific_Feature_Modelling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multi-Modal_Learning_With_Missing_Modality_via_Shared-Specific_Feature_Modelling_CVPR_2023_paper.html | CVPR 2023 | null |
Panoptic Compositional Feature Field for Editable Scene Rendering With Network-Inferred Labels via Metric Learning | Xinhua Cheng, Yanmin Wu, Mengxi Jia, Qian Wang, Jian Zhang | Despite neural implicit representations demonstrating impressive high-quality view synthesis capacity, decomposing such representations into objects for instance-level editing is still challenging. Recent works learn object-compositional representations supervised by ground truth instance annotations and produce promising scene editing results. However, ground truth annotations are manually labeled and expensive in practice, which limits their usage in real-world scenes. In this work, we attempt to learn an object-compositional neural implicit representation for editable scene rendering by leveraging labels inferred from the off-the-shelf 2D panoptic segmentation networks instead of the ground truth annotations. We propose a novel framework named Panoptic Compositional Feature Field (PCFF), which introduces an instance quadruplet metric learning to build a discriminating panoptic feature space for reliable scene editing. In addition, we propose semantic-related strategies to further exploit the correlations between semantic and appearance attributes for achieving better rendering results. Experiments on multiple scene datasets including ScanNet, Replica, and ToyDesk demonstrate that our proposed method achieves superior performance for novel view synthesis and produces convincing real-world scene editing results. The code will be available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_Panoptic_Compositional_Feature_Field_for_Editable_Scene_Rendering_With_Network-Inferred_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cheng_Panoptic_Compositional_Feature_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_Panoptic_Compositional_Feature_Field_for_Editable_Scene_Rendering_With_Network-Inferred_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_Panoptic_Compositional_Feature_Field_for_Editable_Scene_Rendering_With_Network-Inferred_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Backdoor Erasing via Connecting Backdoor and Adversarial Attacks | Bingxu Mu, Zhenxing Niu, Le Wang, Xue Wang, Qiguang Miao, Rong Jin, Gang Hua | Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, in this paper we find an intriguing connection between them: for a model planted with backdoors, we observe that its adversarial examples have similar behaviors as its triggered samples, i.e., both activate the same subset of DNN neurons. It indicates that planting a backdoor into a model will significantly affect the model's adversarial examples. Based on this observations, a novel Progressive Backdoor Erasing (PBE) algorithm is proposed to progressively purify the infected model by leveraging untargeted adversarial attacks. Different from previous backdoor defense methods, one significant advantage of our approach is that it can erase backdoor even when the additional clean dataset is unavailable. We empirically show that, against 5 state-of-the-art backdoor attacks, our AFT can effectively erase the backdoor triggers without obvious performance degradation on clean samples and significantly outperforms existing defense methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mu_Progressive_Backdoor_Erasing_via_Connecting_Backdoor_and_Adversarial_Attacks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mu_Progressive_Backdoor_Erasing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2202.06312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mu_Progressive_Backdoor_Erasing_via_Connecting_Backdoor_and_Adversarial_Attacks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mu_Progressive_Backdoor_Erasing_via_Connecting_Backdoor_and_Adversarial_Attacks_CVPR_2023_paper.html | CVPR 2023 | null |
LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction | Zhaoyun Jiang, Jiaqi Guo, Shizhao Sun, Huayu Deng, Zhongkai Wu, Vuksan Mijovic, Zijiang James Yang, Jian-Guang Lou, Dongmei Zhang | Conditional graphic layout generation, which generates realistic layouts according to user constraints, is a challenging task that has not been well-studied yet. First, there is limited discussion about how to handle diverse user constraints flexibly and uniformly. Second, to make the layouts conform to user constraints, existing work often sacrifices generation quality significantly. In this work, we propose LayoutFormer++ to tackle the above problems. First, to flexibly handle diverse constraints, we propose a constraint serialization scheme, which represents different user constraints as sequences of tokens with a predefined format. Then, we formulate conditional layout generation as a sequence-to-sequence transformation, and leverage encoder-decoder framework with Transformer as the basic architecture. Furthermore, to make the layout better meet user requirements without harming quality, we propose a decoding space restriction strategy. Specifically, we prune the predicted distribution by ignoring the options that definitely violate user constraints and likely result in low-quality layouts, and make the model samples from the restricted distribution. Experiments demonstrate that LayoutFormer++ outperforms existing approaches on all the tasks in terms of both better generation quality and less constraint violation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_LayoutFormer_Conditional_Graphic_Layout_Generation_via_Constraint_Serialization_and_Decoding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_LayoutFormer_Conditional_Graphic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_LayoutFormer_Conditional_Graphic_Layout_Generation_via_Constraint_Serialization_and_Decoding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_LayoutFormer_Conditional_Graphic_Layout_Generation_via_Constraint_Serialization_and_Decoding_CVPR_2023_paper.html | CVPR 2023 | null |
DisWOT: Student Architecture Search for Distillation WithOut Training | Peijie Dong, Lujun Li, Zimian Wei | Knowledge distillation (KD) is an effective training strategy to improve the lightweight student models under the guidance of cumbersome teachers. However, the large architecture difference across the teacher-student pairs limits the distillation gains. In contrast to previous adaptive distillation methods to reduce the teacher-student gap, we explore a novel training-free framework to search for the best student architectures for a given teacher. Our work first empirically show that the optimal model under vanilla training cannot be the winner in distillation. Secondly, we find that the similarity of feature semantics and sample relations between random-initialized teacher-student networks have good correlations with final distillation performances. Thus, we efficiently measure similarity matrixs conditioned on the semantic activation maps to select the optimal student via an evolutionary algorithm without any training. In this way, our student architecture search for Distillation WithOut Training (DisWOT) significantly improves the performance of the model in the distillation stage with at least 180x training acceleration. Additionally, we extend similarity metrics in DisWOT as new distillers and KD-based zero-proxies. Our experiments on CIFAR, ImageNet and NAS-Bench-201 demonstrate that our technique achieves state-of-the-art results on different search spaces. Our project and code are available at https://lilujunai.github.io/DisWOT-CVPR2023/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_DisWOT_Student_Architecture_Search_for_Distillation_WithOut_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_DisWOT_Student_Architecture_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15678 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_DisWOT_Student_Architecture_Search_for_Distillation_WithOut_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_DisWOT_Student_Architecture_Search_for_Distillation_WithOut_Training_CVPR_2023_paper.html | CVPR 2023 | null |
Stare at What You See: Masked Image Modeling Without Reconstruction | Hongwei Xue, Peng Gao, Hongyang Li, Yu Qiao, Hao Sun, Houqiang Li, Jiebo Luo | Masked Autoencoders (MAE) have been prevailing paradigms for large-scale vision representation pre-training. By reconstructing masked image patches from a small portion of visible image regions, MAE forces the model to infer semantic correlation within an image. Recently, some approaches apply semantic-rich teacher models to extract image features as the reconstruction target, leading to better performance. However, unlike the low-level features such as pixel values, we argue the features extracted by powerful teacher models already encode rich semantic correlation across regions in an intact image. This raises one question: is reconstruction necessary in Masked Image Modeling (MIM) with a teacher model? In this paper, we propose an efficient MIM paradigm named MaskAlign. MaskAlign simply learns the consistency of visible patch feature extracted by the student model and intact image features extracted by the teacher model. To further advance the performance and tackle the problem of input inconsistency between the student and teacher model, we propose a Dynamic Alignment (DA) module to apply learnable alignment. Our experimental results demonstrate that masked modeling does not lose effectiveness even without reconstruction on masked regions. Combined with Dynamic Alignment, MaskAlign can achieve state-of-the-art performance with much higher efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xue_Stare_at_What_You_See_Masked_Image_Modeling_Without_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xue_Stare_at_What_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.08887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_Stare_at_What_You_See_Masked_Image_Modeling_Without_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_Stare_at_What_You_See_Masked_Image_Modeling_Without_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Joint Visual Grounding and Tracking With Natural Language Specification | Li Zhou, Zikun Zhou, Kaige Mao, Zhenyu He | Tracking by natural language specification aims to locate the referred target in a sequence based on the natural language description. Existing algorithms solve this issue in two steps, visual grounding and tracking, and accordingly deploy the separated grounding model and tracking model to implement these two steps, respectively. Such a separated framework overlooks the link between visual grounding and tracking, which is that the natural language descriptions provide global semantic cues for localizing the target for both two steps. Besides, the separated framework can hardly be trained end-to-end. To handle these issues, we propose a joint visual grounding and tracking framework, which reformulates grounding and tracking as a unified task: localizing the referred target based on the given visual-language references. Specifically, we propose a multi-source relation modeling module to effectively build the relation between the visual-language references and the test image. In addition, we design a temporal modeling module to provide a temporal clue with the guidance of the global semantic information for our model, which effectively improves the adaptability to the appearance variations of the target. Extensive experimental results on TNL2K, LaSOT, OTB99, and RefCOCOg demonstrate that our method performs favorably against state-of-the-art algorithms for both tracking and grounding. Code is available at https://github.com/lizhou-cs/JointNLT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Joint_Visual_Grounding_and_Tracking_With_Natural_Language_Specification_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.12027 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Joint_Visual_Grounding_and_Tracking_With_Natural_Language_Specification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Joint_Visual_Grounding_and_Tracking_With_Natural_Language_Specification_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Kaleidoscopic Space Sculpting | Byeongjoo Ahn, Michael De Zeeuw, Ioannis Gkioulekas, Aswin C. Sankaranarayanan | We introduce a method that recovers full-surround 3D reconstructions from a single kaleidoscopic image using a neural surface representation. Full-surround 3D reconstruction is critical for many applications, such as augmented and virtual reality. A kaleidoscope, which uses a single camera and multiple mirrors, is a convenient way of achieving full-surround coverage, as it redistributes light directions and thus captures multiple viewpoints in a single image. This enables single-shot and dynamic full-surround 3D reconstruction. However, using a kaleidoscopic image for multi-view stereo is challenging, as we need to decompose the image into multi-view images by identifying which pixel corresponds to which virtual camera, a process we call labeling. To address this challenge, pur approach avoids the need to explicitly estimate labels, but instead "sculpts" a neural surface representation through the careful use of silhouette, background, foreground, and texture information present in the kaleidoscopic image. We demonstrate the advantages of our method in a range of simulated and real experiments, on both static and dynamic scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ahn_Neural_Kaleidoscopic_Space_Sculpting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ahn_Neural_Kaleidoscopic_Space_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ahn_Neural_Kaleidoscopic_Space_Sculpting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ahn_Neural_Kaleidoscopic_Space_Sculpting_CVPR_2023_paper.html | CVPR 2023 | null |
Few-Shot Semantic Image Synthesis With Class Affinity Transfer | Marlène Careil, Jakob Verbeek, Stéphane Lathuilière | Semantic image synthesis aims to generate photo realistic images given a semantic segmentation map. Despite much recent progress, training them still requires large datasets of images annotated with per-pixel label maps that are extremely tedious to obtain. To alleviate the high annotation cost, we propose a transfer method that leverages a model trained on a large source dataset to improve the learning ability on small target datasets via estimated pairwise relations between source and target classes. The class affinity matrix is introduced as a first layer to the source model to make it compatible with the target label maps, and the source model is then further fine-tuned for the target domain. To estimate the class affinities we consider different approaches to leverage prior knowledge: semantic segmentation on the source domain, textual label embeddings, and self-supervised vision features. We apply our approach to GAN-based and diffusion-based architectures for semantic synthesis. Our experiments show that the different ways to estimate class affinity can effectively combined, and that our approach significantly improves over existing state-of-the-art transfer approaches for generative image models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Careil_Few-Shot_Semantic_Image_Synthesis_With_Class_Affinity_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Careil_Few-Shot_Semantic_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Careil_Few-Shot_Semantic_Image_Synthesis_With_Class_Affinity_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Careil_Few-Shot_Semantic_Image_Synthesis_With_Class_Affinity_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
Implicit Identity Driven Deepfake Face Swapping Detection | Baojin Huang, Zhongyuan Wang, Jifan Yang, Jiaxin Ai, Qin Zou, Qian Wang, Dengpan Ye | In this paper, we consider the face swapping detection from the perspective of face identity. Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake. We argue that the fake face contains the explicit identity and implicit identity, which respectively corresponds to the identity of the source face and target face during face swapping. Note that the explicit identities of faces can be extracted by regular face recognizers. Particularly, the implicit identity of real face is consistent with the its explicit identity. Thus the difference between explicit and implicit identity of face facilitates face swapping detection. Following this idea, we propose a novel implicit identity driven framework for face swapping detection. Specifically, we design an explicit identity contrast (EIC) loss and an implicit identity exploration (IIE) loss, which supervises a CNN backbone to embed face images into the implicit identity space. Under the guidance of EIC, real samples are pulled closer to their explicit identities, while fake samples are pushed away from their explicit identities. Moreover, IIE is derived from the margin-based classification loss function, which encourages the fake faces with known target identities to enjoy intra-class compactness and inter-class diversity. Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art counterparts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Implicit_Identity_Driven_Deepfake_Face_Swapping_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Implicit_Identity_Driven_Deepfake_Face_Swapping_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Implicit_Identity_Driven_Deepfake_Face_Swapping_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation | Yixin Zhang, Zilei Wang, Weinan He | This work focuses on a practical knowledge transfer task defined as Source-Free Unsupervised Domain Adaptation (SFUDA), where only a well-trained source model and unlabeled target data are available. To fully utilize source knowledge, we propose to transfer the class relationship, which is domain-invariant but still under-explored in previous works. To this end, we first regard the classifier weights of the source model as class prototypes to compute class relationship, and then propose a novel probability-based similarity between target-domain samples by embedding the source-domain class relationship, resulting in Class Relationship embedded Similarity (CRS). Here the inter-class term is particularly considered in order to more accurately represent the similarity between two samples, in which the source prior of class relationship is utilized by weighting. Finally, we propose to embed CRS into contrastive learning in a unified form. Here both class-aware and instance discrimination contrastive losses are employed, which are complementary to each other. We combine the proposed method with existing representative methods to evaluate its efficacy in multiple SFUDA settings. Extensive experimental results reveal that our method can achieve state-of-the-art performance due to the transfer of domain-invariant class relationship. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Class_Relationship_Embedded_Learning_for_Source-Free_Unsupervised_Domain_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Class_Relationship_Embedded_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Class_Relationship_Embedded_Learning_for_Source-Free_Unsupervised_Domain_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Class_Relationship_Embedded_Learning_for_Source-Free_Unsupervised_Domain_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning | Haiyu Wu, Grace Bezold, Aman Bhatta, Kevin W. Bowyer | Face attribute research has so far used only simple binary attributes for facial hair; e.g., beard / no beard. We have created a new, more descriptive facial hair annotation scheme and applied it to create a new facial hair attribute dataset, FH37K. Face attribute research also so far has not dealt with logical consistency and completeness. For example, in prior research, an image might be classified as both having no beard and also having a goatee (a type of beard). We show that the test accuracy of previous classification methods on facial hair attribute classification drops significantly if logical consistency of classifications is enforced. We propose a logically consistent prediction loss, LCPLoss, to aid learning of logical consistency across attributes, and also a label compensation training strategy to eliminate the problem of no positive prediction across a set of related attributes. Using an attribute classifier trained on FH37K, we investigate how facial hair affects face recognition accuracy, including variation across demographics. Results show that similarity and difference in facial hairstyle have important effects on the impostor and genuine score distributions in face recognition. The code is at https:// github.com/ HaiyuWu/ facial hair logical. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Logical_Consistency_and_Greater_Descriptive_Power_for_Facial_Hair_Attribute_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Logical_Consistency_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.11102 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Logical_Consistency_and_Greater_Descriptive_Power_for_Facial_Hair_Attribute_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Logical_Consistency_and_Greater_Descriptive_Power_for_Facial_Hair_Attribute_CVPR_2023_paper.html | CVPR 2023 | null |
One-to-Few Label Assignment for End-to-End Dense Detection | Shuai Li, Minghan Li, Ruihuang Li, Chenhang He, Lei Zhang | One-to-one (o2o) label assignment plays a key role for transformer based end-to-end detection, and it has been recently introduced in fully convolutional detectors for lightweight end-to-end dense detection. However, o2o can largely degrade the feature learning performance due to the limited number of positive samples. Though extra positive samples can be introduced to mitigate this issue, the computation of self- and cross- attentions among anchors prevents its practical application to dense and fully convolutional detectors. In this work, we propose a simple yet effective one-to-few (o2f) label assignment strategy for end-to-end dense detection. Apart from defining one positive and many negative anchors for each object, we define several soft anchors, which serve as positive and negative samples simultaneously. The positive and negative weights of these soft anchors are dynamically adjusted during training so that they can contribute more to 'representation learning' in the early training stage and contribute more to 'duplicated prediction removal' in the later stage. The detector trained in this way can not only learn a strong feature representation but also perform end-to-end detection. Experiments on COCO and CrowdHuman datasets demonstrate the effectiveness of the proposed o2f scheme. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_One-to-Few_Label_Assignment_for_End-to-End_Dense_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_One-to-Few_Label_Assignment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11567 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_One-to-Few_Label_Assignment_for_End-to-End_Dense_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_One-to-Few_Label_Assignment_for_End-to-End_Dense_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Spatio-Temporal Pixel-Level Contrastive Learning-Based Source-Free Domain Adaptation for Video Semantic Segmentation | Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati, Alejandro Galindo, Vishal M. Patel | Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data. However, the access to source data is often restricted or infeasible in real-world scenarios. Under the source data restrictive circumstances, UDA is less practical. To address this, recent works have explored solutions under the Source-Free Domain Adaptation (SFDA) setup, which aims to adapt a source-trained model to the target domain without accessing source data. Still, existing SFDA approaches use only image-level information for adaptation, making them sub-optimal in video applications. This paper studies SFDA for Video Semantic Segmentation (VSS), where temporal information is leveraged to address video adaptation. Specifically, we propose Spatio-Temporal Pixel-Level (STPL) contrastive learning, a novel method that takes full advantage of spatio-temporal information to tackle the absence of source data better. STPL explicitly learns semantic correlations among pixels in the spatio-temporal space, providing strong self-supervision for adaptation to the unlabeled target domain. Extensive experiments show that STPL achieves state-of-the-art performance on VSS benchmarks compared to current UDA and SFDA approaches. Code is available at: https://github.com/shaoyuanlo/STPL | https://openaccess.thecvf.com/content/CVPR2023/papers/Lo_Spatio-Temporal_Pixel-Level_Contrastive_Learning-Based_Source-Free_Domain_Adaptation_for_Video_Semantic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lo_Spatio-Temporal_Pixel-Level_Contrastive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14361 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lo_Spatio-Temporal_Pixel-Level_Contrastive_Learning-Based_Source-Free_Domain_Adaptation_for_Video_Semantic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lo_Spatio-Temporal_Pixel-Level_Contrastive_Learning-Based_Source-Free_Domain_Adaptation_for_Video_Semantic_CVPR_2023_paper.html | CVPR 2023 | null |
InternImage: Exploring Large-Scale Vision Foundation Models With Deformable Convolutions | Wenhai Wang, Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li, Xiaogang Wang, Yu Qiao | Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_InternImage_Exploring_Large-Scale_Vision_Foundation_Models_With_Deformable_Convolutions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_InternImage_Exploring_Large-Scale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.05778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_InternImage_Exploring_Large-Scale_Vision_Foundation_Models_With_Deformable_Convolutions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_InternImage_Exploring_Large-Scale_Vision_Foundation_Models_With_Deformable_Convolutions_CVPR_2023_paper.html | CVPR 2023 | null |
DAA: A Delta Age AdaIN Operation for Age Estimation via Binary Code Transformer | Ping Chen, Xingpeng Zhang, Ye Li, Ju Tao, Bin Xiao, Bing Wang, Zongjie Jiang | Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation. We let the input of transfer learning as the binary code of age natural number to obtain continuous age feature information. The learned two groups of values in Binary code mapping are corresponding to the mean and standard deviation of the comparison ages. In summary, our method consists of four parts: FaceEncoder, DAA operation, Binary code mapping, and AgeDecoder modules. After getting the delta age via AgeDecoder, we take the average value of all comparison ages and delta ages as the predicted age. Compared with state-of-the-art methods, our method achieves better performance with fewer parameters on multiple facial age datasets. Code is available at https://github.com/redcping/Delta_Age_AdaIN | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_DAA_A_Delta_Age_AdaIN_Operation_for_Age_Estimation_via_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.07929 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DAA_A_Delta_Age_AdaIN_Operation_for_Age_Estimation_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DAA_A_Delta_Age_AdaIN_Operation_for_Age_Estimation_via_CVPR_2023_paper.html | CVPR 2023 | null |
Fake It Till You Make It: Learning Transferable Representations From Synthetic ImageNet Clones | Mert Bülent Sarıyıldız, Karteek Alahari, Diane Larlus, Yannis Kalantidis | Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study. More importantly, we show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer. Project page: https://europe.naverlabs.com/imagenet-sd | https://openaccess.thecvf.com/content/CVPR2023/papers/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sariyildiz_Fake_It_Till_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.html | CVPR 2023 | null |
Mind the Label Shift of Augmentation-Based Graph OOD Generalization | Junchi Yu, Jian Liang, Ran He | Out-of-distribution (OOD) generalization is an important issue for Graph Neural Networks (GNNs). Recent works employ different graph editions to generate augmented environments and learn an invariant GNN for generalization. However, the graph structural edition inevitably alters the graph label. This causes the label shift in augmentations and brings inconsistent predictive relationships among augmented environments. To address this issue, we propose LiSA, which generates label-invariant augmentations to facilitate graph OOD generalization. Instead of resorting to graph editions, LiSA exploits Label-invariant Subgraphs of the training graphs to construct Augmented environments. Specifically, LiSA first designs the variational subgraph generators to efficiently extract locally predictive patterns and construct multiple label-invariant subgraphs. Then, the subgraphs produced by different generators are collected to build different augmented environments. To promote diversity among augmented environments, LiSA further introduces a tractable energy-based regularization to enlarge pair-wise distances between the distributions of environments. In this manner, LiSA generates diverse augmented environments with a consistent predictive relationship to facilitate learning an invariant GNN. Extensive experiments on node-level and graph-level OOD benchmarks show that LiSA achieves impressive generalization performance with different GNN backbones. Code is available on https://github.com/Samyu0304/LiSA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Mind_the_Label_Shift_of_Augmentation-Based_Graph_OOD_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Mind_the_Label_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Mind_the_Label_Shift_of_Augmentation-Based_Graph_OOD_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Mind_the_Label_Shift_of_Augmentation-Based_Graph_OOD_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Intrinsic Image Decomposition With LiDAR Intensity | Shogo Sato, Yasuhiro Yao, Taiga Yoshida, Takuhiro Kaneko, Shingo Ando, Jun Shimamura | Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sato_Unsupervised_Intrinsic_Image_Decomposition_With_LiDAR_Intensity_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sato_Unsupervised_Intrinsic_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10820 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sato_Unsupervised_Intrinsic_Image_Decomposition_With_LiDAR_Intensity_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sato_Unsupervised_Intrinsic_Image_Decomposition_With_LiDAR_Intensity_CVPR_2023_paper.html | CVPR 2023 | null |
HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_HIER_Metric_Learning_Beyond_Class_Labels_via_Hierarchical_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_HIER_Metric_Learning_Beyond_Class_Labels_via_Hierarchical_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
Diffusion Probabilistic Model Made Slim | Xingyi Yang, Daquan Zhou, Jiashi Feng, Xinchao Wang | Despite the visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms. Prior methods towards efficient DPM, however, have largely focused on accelerating the testing yet overlooked their huge complexity and size. In this paper, we make a dedicated attempt to lighten DPM while striving to preserve its favourable performance. We start by training a small-sized latent diffusion model (LDM) from scratch but observe a significant fidelity drop in the synthetic images. Through a thorough assessment, we find that DPM is intrinsically biased against high-frequency generation, and learns to recover different frequency components at different time-steps. These properties make compact networks unable to represent frequency dynamics with accurate high-frequency estimation. Towards this end, we introduce a customized design for slim DPM, which we term as Spectral Diffusion (SD), for lightweight image synthesis. SD incorporates wavelet gating in its architecture to enable frequency dynamic feature extraction at every reverse steps, and conducts spectrum-aware distillation to promote high-frequency recovery by inverse weighting the objective based on spectrum magnitudes. Experimental results demonstrate that, SD achieves 8-18x computational complexity reduction as compared to the latent diffusion models on a series of conditional and unconditional image generation tasks while retaining competitive image fidelity. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Diffusion_Probabilistic_Model_Made_Slim_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Diffusion_Probabilistic_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.17106 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Diffusion_Probabilistic_Model_Made_Slim_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Diffusion_Probabilistic_Model_Made_Slim_CVPR_2023_paper.html | CVPR 2023 | null |
Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | Junyi Zhu, Xingchen Ma, Matthew B. Blaschko | Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients could be non-identically distributed and have different sizes. Personalized Federated Learning (PFL) attempts to solve this challenge via locally adapted models. In this work, we present a novel framework for PFL based on hierarchical Bayesian modeling and variational inference. A global model is introduced as a latent variable to augment the joint distribution of clients' parameters and capture the common trends of different clients, optimization is derived based on the principle of maximizing the marginal likelihood and conducted using variational expectation maximization. Our algorithm gives rise to a closed-form estimation of a confidence value which comprises the uncertainty of clients' parameters and local model deviations from the global model. The confidence value is used to weigh clients' parameters in the aggregation stage and adjust the regularization effect of the global model. We evaluate our method through extensive empirical studies on multiple datasets. Experimental results show that our approach obtains competitive results under mild heterogeneous circumstances while significantly outperforming state-of-the-art PFL frameworks in highly heterogeneous settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Confidence-Aware_Personalized_Federated_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection | Chuandong Liu, Chenqiang Gao, Fangcen Liu, Pengcheng Li, Deyu Meng, Xinbo Gao | State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. However, such 3D annotations are often expensive and time-consuming, which may not be practical for real applications. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. Current pseudo-labeling-based SSL object detection methods mainly adopt a teacher-student framework, with a single fixed threshold strategy to generate supervision signals, which inevitably brings confused supervision when guiding the student network training. Besides, the data augmentation of the point cloud in the typical teacher-student framework is too weak, and only contains basic down sampling and flip-and-shift (i.e., rotate and scaling), which hinders the effective learning of feature information. Hence, we address these issues by introducing a novel approach of Hierarchical Supervision and Shuffle Data Augmentation (HSSDA), which is a simple yet effective teacher-student framework. The teacher network generates more reasonable supervision for the student network by designing a dynamic dual-threshold strategy. Besides, the shuffle data augmentation strategy is designed to strengthen the feature representation ability of the student network. Extensive experiments show that HSSDA consistently outperforms the recent state-of-the-art methods on different datasets. The code will be released at https://github.com/azhuantou/HSSDA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Hierarchical_Supervision_and_Shuffle_Data_Augmentation_for_3D_Semi-Supervised_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Hierarchical_Supervision_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01464 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Hierarchical_Supervision_and_Shuffle_Data_Augmentation_for_3D_Semi-Supervised_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Hierarchical_Supervision_and_Shuffle_Data_Augmentation_for_3D_Semi-Supervised_Object_CVPR_2023_paper.html | CVPR 2023 | null |
Interactive and Explainable Region-Guided Radiology Report Generation | Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert | The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://github.com/ttanida/rgrg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tanida_Interactive_and_Explainable_Region-Guided_Radiology_Report_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tanida_Interactive_and_Explainable_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tanida_Interactive_and_Explainable_Region-Guided_Radiology_Report_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tanida_Interactive_and_Explainable_Region-Guided_Radiology_Report_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
MED-VT: Multiscale Encoder-Decoder Video Transformer With Application To Object Segmentation | Rezaul Karim, He Zhao, Richard P. Wildes, Mennatullah Siam | Multiscale video transformers have been explored in a wide variety of vision tasks. To date, however, the multiscale processing has been confined to the encoder or decoder alone. We present a unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in videos. Multiscale representation at both encoder and decoder yields key benefits of implicit extraction of spatiotemporal features (i.e. without reliance on input optical flow) as well as temporal consistency at encoding and coarse-to-fine detection for high-level (e.g. object) semantics to guide precise localization at decoding. Moreover, we propose a transductive learning scheme through many-to-many label propagation to provide temporally consistent predictions.We showcase our Multiscale Encoder-Decoder Video Transformer (MED-VT) on Automatic Video Object Segmentation (AVOS) and actor/action segmentation, where we outperform state-of-the-art approaches on multiple benchmarks using only raw images, without using optical flow. | https://openaccess.thecvf.com/content/CVPR2023/papers/Karim_MED-VT_Multiscale_Encoder-Decoder_Video_Transformer_With_Application_To_Object_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Karim_MED-VT_Multiscale_Encoder-Decoder_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Karim_MED-VT_Multiscale_Encoder-Decoder_Video_Transformer_With_Application_To_Object_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Karim_MED-VT_Multiscale_Encoder-Decoder_Video_Transformer_With_Application_To_Object_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces | Yiqun Wang, Ivan Skorokhodov, Peter Wonka | A signed distance function (SDF) parametrized by an MLP is a common ingredient of neural surface reconstruction. We build on the successful recent method NeuS to extend it by three new components. The first component is to borrow the tri-plane representation from EG3D and represent signed distance fields as a mixture of tri-planes and MLPs instead of representing it with MLPs only. Using tri-planes leads to a more expressive data structure but will also introduce noise in the reconstructed surface. The second component is to use a new type of positional encoding with learnable weights to combat noise in the reconstruction process. We divide the features in the tri-plane into multiple frequency scales and modulate them with sin and cos functions of different frequencies. The third component is to use learnable convolution operations on the tri-plane features using self-attention convolution to produce features with different frequency bands. The experiments show that PET-NeuS achieves high-fidelity surface reconstruction on standard datasets. Following previous work and using the Chamfer metric as the most important way to measure surface reconstruction quality, we are able to improve upon the NeuS baseline by 57% on Nerf-synthetic (0.84 compared to 1.97) and by 15.5% on DTU (0.71 compared to 0.84). The qualitative evaluation reveals how our method can better control the interference of high-frequency noise. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_PET-NeuS_Positional_Encoding_Tri-Planes_for_Neural_Surfaces_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_PET-NeuS_Positional_Encoding_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PET-NeuS_Positional_Encoding_Tri-Planes_for_Neural_Surfaces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PET-NeuS_Positional_Encoding_Tri-Planes_for_Neural_Surfaces_CVPR_2023_paper.html | CVPR 2023 | null |
ZegCLIP: Towards Adapting CLIP for Zero-Shot Semantic Segmentation | Ziqin Zhou, Yinjie Lei, Bowen Zhang, Lingqiao Liu, Yifan Liu | Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a wo-stage scheme. The general idea is to first generate class-agnostic region proposals and then feed the cropped proposal regions to CLIP to utilize its image-level zero-shot classification capability. While effective, such a scheme requires two image encoders, one for proposal generation and one for CLIP, leading to a complicated pipeline and high computational cost. In this work, we pursue a simpler-and-efficient one-stage solution that directly extends CLIP's zero-shot prediction capability from image to pixel level. Our investigation starts with a straightforward extension as our baseline that generates semantic masks by comparing the similarity between text and patch embeddings extracted from CLIP. However, such a paradigm could heavily overfit the seen classes and fail to generalize to unseen classes. To handle this issue, we propose three simple-but-effective designs and figure out that they can significantly retain the inherent zero-shot capacity of CLIP and improve pixel-level generalization ability. Incorporating those modifications leads to an efficient zero-shot semantic segmentation system called ZegCLIP. Through extensive experiments on three public benchmarks, ZegCLIP demonstrates superior performance, outperforming the state-of-the-art methods by a large margin under both "inductive" and "transductive" zero-shot settings. In addition, compared with the two-stage method, our one-stage ZegCLIP achieves a speedup of about 5 times faster during inference. We release the code at https://github.com/ZiqinZhou66/ZegCLIP.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_ZegCLIP_Towards_Adapting_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_ZegCLIP_Towards_Adapting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03588 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_ZegCLIP_Towards_Adapting_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_ZegCLIP_Towards_Adapting_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
AdaptiveMix: Improving GAN Training via Feature Space Shrinkage | Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng | Due to the outstanding capability for data generation, Generative Adversarial Networks (GANs) have attracted considerable attention in unsupervised learning. However, training GANs is difficult, since the training distribution is dynamic for the discriminator, leading to unstable image representation. In this paper, we address the problem of training GANs from a novel perspective, i.e., robust image classification. Motivated by studies on robust image representation, we propose a simple yet effective module, namely AdaptiveMix, for GANs, which shrinks the regions of training data in the image representation space of the discriminator. Considering it is intractable to directly bound feature space, we propose to construct hard samples and narrow down the feature distance between hard and easy samples. The hard samples are constructed by mixing a pair of training images. We evaluate the effectiveness of our AdaptiveMix with widely-used and state-of-the-art GAN architectures. The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples. We also show that our AdaptiveMix can be further applied to image classification and Out-Of-Distribution (OOD) detection tasks, by equipping it with state-of-the-art methods. Extensive experiments on seven publicly available datasets show that our method effectively boosts the performance of baselines. The code is publicly available at https://github.com/WentianZhang-ML/AdaptiveMix. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_AdaptiveMix_Improving_GAN_Training_via_Feature_Space_Shrinkage_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_AdaptiveMix_Improving_GAN_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_AdaptiveMix_Improving_GAN_Training_via_Feature_Space_Shrinkage_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_AdaptiveMix_Improving_GAN_Training_via_Feature_Space_Shrinkage_CVPR_2023_paper.html | CVPR 2023 | null |
Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models To Learn Any Unseen Style | Haoming Lu, Hazarapet Tunanyan, Kai Wang, Shant Navasardyan, Zhangyang Wang, Humphrey Shi | Diffusion models have demonstrated impressive capability of text-conditioned image synthesis, and broader application horizons are emerging by personalizing those pretrained diffusion models toward generating some specialized target object or style. In this paper, we aim to learn an unseen style by simply fine-tuning a pre-trained diffusion model with a handful of images (e.g., less than 10), so that the fine-tuned model can generate high-quality images of arbitrary objects in this style. Such extremely lowshot fine-tuning is accomplished by a novel toolkit of finetuning techniques, including text-to-image customized data augmentations, a content loss to facilitate content-style disentanglement, and sparse updating that focuses on only a few time steps. Our framework, dubbed Specialist Diffusion, is plug-and-play to existing diffusion model backbones and other personalization techniques. We demonstrate it to outperform the latest few-shot personalization alternatives of diffusion models such as Textual Inversion and DreamBooth, in terms of learning highly sophisticated styles with ultra-sample-efficient tuning. We further show that Specialist Diffusion can be integrated on top of textual inversion to boost performance further, even on highly unusual styles. Our codes are available at: https://github.com/Picsart-AI-Research/Specialist-Diffusion | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Specialist_Diffusion_Plug-and-Play_Sample-Efficient_Fine-Tuning_of_Text-to-Image_Diffusion_Models_To_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_Specialist_Diffusion_Plug-and-Play_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Specialist_Diffusion_Plug-and-Play_Sample-Efficient_Fine-Tuning_of_Text-to-Image_Diffusion_Models_To_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Specialist_Diffusion_Plug-and-Play_Sample-Efficient_Fine-Tuning_of_Text-to-Image_Diffusion_Models_To_CVPR_2023_paper.html | CVPR 2023 | null |
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | Mingu Kang, Heon Song, Seonwook Park, Donggeun Yoo, Sérgio Pereira | Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements under diverse settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Benchmarking_Self-Supervised_Learning_on_Diverse_Pathology_Datasets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Benchmarking_Self-Supervised_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Benchmarking_Self-Supervised_Learning_on_Diverse_Pathology_Datasets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Benchmarking_Self-Supervised_Learning_on_Diverse_Pathology_Datasets_CVPR_2023_paper.html | CVPR 2023 | null |
Planning-Oriented Autonomous Driving | Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, Hongyang Li | Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative errors or deficient task coordination. Instead, we argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car. Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning. We introduce Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network. It is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective. Tasks are communicated with unified query interfaces to facilitate each other toward planning. We instantiate UniAD on the challenging nuScenes benchmark. With extensive ablations, the effectiveness of using such a philosophy is proven by substantially outperforming previous state-of-the-arts in all aspects. Code and models are public. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Planning-Oriented_Autonomous_Driving_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Planning-Oriented_Autonomous_Driving_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.10156 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Planning-Oriented_Autonomous_Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Planning-Oriented_Autonomous_Driving_CVPR_2023_paper.html | CVPR 2023 | null |
HyperCUT: Video Sequence From a Single Blurry Image Using Unsupervised Ordering | Bang-Dang Pham, Phong Tran, Anh Tran, Cuong Pham, Rang Nguyen, Minh Hoai | We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A critical issue disturbing the training of an image-to-video model is the ambiguity of the frame ordering since both the forward and backward sequences are plausible solutions. This paper proposes an effective self-supervised ordering scheme that allows training high-quality image-to-video deblurring models. Unlike previous methods that rely on order-invariant losses, we assign an explicit order for each video sequence, thus avoiding the order-ambiguity issue. Specifically, we map each video sequence to a vector in a latent high-dimensional space so that there exists a hyperplane such that for every video sequence, the vectors extracted from it and its reversed sequence are on different sides of the hyperplane. The side of the vectors will be used to define the order of the corresponding sequence. Last but not least, we propose a real-image dataset for the image-to-video deblurring problem that covers a variety of popular domains, including face, hand, and street. Extensive experimental results confirm the effectiveness of our method. Code and data are available at https://github.com/VinAIResearch/HyperCUT.git | https://openaccess.thecvf.com/content/CVPR2023/papers/Pham_HyperCUT_Video_Sequence_From_a_Single_Blurry_Image_Using_Unsupervised_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pham_HyperCUT_Video_Sequence_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01686 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pham_HyperCUT_Video_Sequence_From_a_Single_Blurry_Image_Using_Unsupervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pham_HyperCUT_Video_Sequence_From_a_Single_Blurry_Image_Using_Unsupervised_CVPR_2023_paper.html | CVPR 2023 | null |
Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders | Zeyang Sha, Xinlei He, Ning Yu, Michael Backes, Yang Zhang | Self-supervised representation learning techniques have been developing rapidly to make full use of unlabeled images. They encode images into rich features that are oblivious to downstream tasks. Behind their revolutionary representation power, the requirements for dedicated model designs and a massive amount of computation resources expose image encoders to the risks of potential model stealing attacks - a cheap way to mimic the well-trained encoder performance while circumventing the demanding requirements. Yet conventional attacks only target supervised classifiers given their predicted labels and/or posteriors, which leaves the vulnerability of unsupervised encoders unexplored. In this paper, we first instantiate the conventional stealing attacks against encoders and demonstrate their severer vulnerability compared with downstream classifiers. To better leverage the rich representation of encoders, we further propose Cont-Steal, a contrastive-learning-based attack, and validate its improved stealing effectiveness in various experiment settings. As a takeaway, we appeal to our community's attention to the intellectual property protection of representation learning techniques, especially to the defenses against encoder stealing attacks like ours. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sha_Cant_Steal_Cont-Steal_Contrastive_Stealing_Attacks_Against_Image_Encoders_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sha_Cant_Steal_Cont-Steal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sha_Cant_Steal_Cont-Steal_Contrastive_Stealing_Attacks_Against_Image_Encoders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sha_Cant_Steal_Cont-Steal_Contrastive_Stealing_Attacks_Against_Image_Encoders_CVPR_2023_paper.html | CVPR 2023 | null |
Document Image Shadow Removal Guided by Color-Aware Background | Ling Zhang, Yinghao He, Qing Zhang, Zheng Liu, Xiaolong Zhang, Chunxia Xiao | Existing works on document image shadow removal mostly depend on learning and leveraging a constant background (the color of the paper) from the image. However, the constant background is less representative and frequently ignores other background colors, such as the printed colors, resulting in distorted results. In this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document. Furthermore, we propose a background-guided document images shadow removal network (BGShadowNet) using the predicted spatially varying background as auxiliary information, which consists of two stages. At Stage I, a background-constrained decoder is designed to promote a coarse result. Then, the coarse result is refined with a background-based attention module (BAModule) to maintain a consistent appearance and a detail improvement module (DEModule) to enhance the texture details at Stage II. Experiments on two benchmark datasets qualitatively and quantitatively validate the superiority of the proposed approach over state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Document_Image_Shadow_Removal_Guided_by_Color-Aware_Background_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Document_Image_Shadow_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Document_Image_Shadow_Removal_Guided_by_Color-Aware_Background_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Document_Image_Shadow_Removal_Guided_by_Color-Aware_Background_CVPR_2023_paper.html | CVPR 2023 | null |
Independent Component Alignment for Multi-Task Learning | Dmitry Senushkin, Nikolay Patakin, Arseny Kuznetsov, Anton Konushin | In a multi-task learning (MTL) setting, a single model is trained to tackle a diverse set of tasks jointly. Despite rapid progress in the field, MTL remains challenging due to optimization issues such as conflicting and dominating gradients. In this work, we propose using a condition number of a linear system of gradients as a stability criterion of an MTL optimization. We theoretically demonstrate that a condition number reflects the aforementioned optimization issues. Accordingly, we present Aligned-MTL, a novel MTL optimization approach based on the proposed criterion, that eliminates instability in the training process by aligning the orthogonal components of the linear system of gradients. While many recent MTL approaches guarantee convergence to a minimum, task trade-offs cannot be specified in advance. In contrast, Aligned-MTL provably converges to an optimal point with pre-defined task-specific weights, which provides more control over the optimization result. Through experiments, we show that the proposed approach consistently improves performance on a diverse set of MTL benchmarks, including semantic and instance segmentation, depth estimation, surface normal estimation, and reinforcement learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Senushkin_Independent_Component_Alignment_for_Multi-Task_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Senushkin_Independent_Component_Alignment_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Senushkin_Independent_Component_Alignment_for_Multi-Task_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Senushkin_Independent_Component_Alignment_for_Multi-Task_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Edges to Shapes to Concepts: Adversarial Augmentation for Robust Vision | Aditay Tripathi, Rishubh Singh, Anirban Chakraborty, Pradeep Shenoy | Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViTB) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tripathi_Edges_to_Shapes_to_Concepts_Adversarial_Augmentation_for_Robust_Vision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tripathi_Edges_to_Shapes_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tripathi_Edges_to_Shapes_to_Concepts_Adversarial_Augmentation_for_Robust_Vision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tripathi_Edges_to_Shapes_to_Concepts_Adversarial_Augmentation_for_Robust_Vision_CVPR_2023_paper.html | CVPR 2023 | null |
ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration | Wei-Ning Hsu, Tal Remez, Bowen Shi, Jacob Donley, Yossi Adi | Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Regeneration, where the goal is not to reconstruct the exact reference clean signal, but to focus on improving certain aspects of speech while not necessarily preserving the rest such as voice. In particular, this paper concerns intelligibility, quality, and video synchronization. We cast the problem as audio-visual speech resynthesis, which is composed of two steps: pseudo audio-visual speech recognition (P-AVSR) and pseudo text-to-speech synthesis (P-TTS). P-AVSR and P-TTS are connected by discrete units derived from a self-supervised speech model. Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR. The proposed model is coined ReVISE. ReVISE is the first high-quality model for in-the-wild video-to-speech synthesis and achieves superior performance on all LRS3 audio-visual regeneration tasks with a single model. To demonstrates its applicability in the real world, ReVISE is also evaluated on EasyCom, an audio-visual benchmark collected under challenging acoustic conditions with only 1.6 hours of training data. Similarly, ReVISE greatly suppresses noise and improves quality. Project page: https://wnhsu.github.io/ReVISE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_paper.html | CVPR 2023 | null |
Improved Distribution Matching for Dataset Condensation | Ganlong Zhao, Guanbin Li, Yipeng Qin, Yizhou Yu | Dataset Condensation aims to condense a large dataset into a smaller one while maintaining its ability to train a well-performing model, thus reducing the storage cost and training effort in deep learning applications. However, conventional dataset condensation methods are optimization-oriented and condense the dataset by performing gradient or parameter matching during model optimization, which is computationally intensive even on small datasets and models. In this paper, we propose a novel dataset condensation method based on distribution matching, which is more efficient and promising. Specifically, we identify two important shortcomings of naive distribution matching (i.e., imbalanced feature numbers and unvalidated embeddings for distance computation) and address them with three novel techniques (i.e., partitioning and expansion augmentation, efficient and enriched model sampling, and class-aware distribution regularization). Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources, thereby scaling data condensation to larger datasets and models. Extensive experiments demonstrate the effectiveness of our method. Codes are available at https://github.com/uitrbn/IDM | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Improved_Distribution_Matching_for_Dataset_Condensation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Improved_Distribution_Matching_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Improved_Distribution_Matching_for_Dataset_Condensation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Improved_Distribution_Matching_for_Dataset_Condensation_CVPR_2023_paper.html | CVPR 2023 | null |
Feature Separation and Recalibration for Adversarial Robustness | Woo Jae Kim, Yoonki Cho, Junsik Jung, Sung-Eui Yoon | Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activations still contain discriminative cues and that with recalibration, they can capture additional useful information for correct model predictions. To this end, we propose a novel, easy-to-plugin approach named Feature Separation and Recalibration (FSR) that recalibrates the malicious, non-robust activations for more robust feature maps through Separation and Recalibration. The Separation part disentangles the input feature map into the robust feature with activations that help the model make correct predictions and the non-robust feature with activations that are responsible for model mispredictions upon adversarial attack. The Recalibration part then adjusts the non-robust activations to restore the potentially useful cues for model predictions. Extensive experiments verify the superiority of FSR compared to traditional deactivation techniques and demonstrate that it improves the robustness of existing adversarial training methods by up to 8.57% with small computational overhead. Codes are available at https://github.com/wkim97/FSR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Feature_Separation_and_Recalibration_for_Adversarial_Robustness_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Feature_Separation_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13846 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Feature_Separation_and_Recalibration_for_Adversarial_Robustness_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Feature_Separation_and_Recalibration_for_Adversarial_Robustness_CVPR_2023_paper.html | CVPR 2023 | null |
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation From 2D Supervision | Xiaoshuai Zhang, Abhijit Kundu, Thomas Funkhouser, Leonidas Guibas, Hao Su, Kyle Genova | We address efficient and structure-aware 3D scene representation from images. Nerflets are our key contribution-- a set of local neural radiance fields that together represent a scene. Each nerflet maintains its own spatial position, orientation, and extent, within which it contributes to panoptic, density, and radiance reconstructions. By leveraging only photometric and inferred panoptic image supervision, we can directly and jointly optimize the parameters of a set of nerflets so as to form a decomposed representation of the scene, where each object instance is represented by a group of nerflets. During experiments with indoor and outdoor environments, we find that nerflets: (1) fit and approximate the scene more efficiently than traditional global NeRFs, (2) allow the extraction of panoptic and photometric renderings from arbitrary views, and (3) enable tasks rare for NeRFs, such as 3D panoptic segmentation and interactive editing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Nerflets_Local_Radiance_Fields_for_Efficient_Structure-Aware_3D_Scene_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Nerflets_Local_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03361 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Nerflets_Local_Radiance_Fields_for_Efficient_Structure-Aware_3D_Scene_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Nerflets_Local_Radiance_Fields_for_Efficient_Structure-Aware_3D_Scene_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP Is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation | Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, Xiaofei He | Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) focus on confident regions. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://github.com/linyq2117/CLIP-ES. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_CLIP_Is_Also_an_Efficient_Segmenter_A_Text-Driven_Approach_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_CLIP_Is_Also_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09506 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_CLIP_Is_Also_an_Efficient_Segmenter_A_Text-Driven_Approach_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_CLIP_Is_Also_an_Efficient_Segmenter_A_Text-Driven_Approach_for_CVPR_2023_paper.html | CVPR 2023 | null |
Slimmable Dataset Condensation | Songhua Liu, Jingwen Ye, Runpeng Yu, Xinchao Wang | Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt an significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Slimmable_Dataset_Condensation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Slimmable_Dataset_Condensation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Slimmable_Dataset_Condensation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Slimmable_Dataset_Condensation_CVPR_2023_paper.html | CVPR 2023 | null |
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising | Junyi Li, Zhilu Zhang, Xiaoyu Liu, Chaoyu Feng, Xiaotao Wang, Lei Lei, Wangmeng Zuo | Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://github.com/nagejacob/SpatiallyAdaptiveSSID. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Spatially_Adaptive_Self-Supervised_Learning_for_Real-World_Image_Denoising_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Spatially_Adaptive_Self-Supervised_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14934 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Spatially_Adaptive_Self-Supervised_Learning_for_Real-World_Image_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Spatially_Adaptive_Self-Supervised_Learning_for_Real-World_Image_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
Data-Free Knowledge Distillation via Feature Exchange and Activation Region Constraint | Shikang Yu, Jiachen Chen, Hu Han, Shuqiang Jiang | Despite the tremendous progress on data-free knowledge distillation (DFKD) based on synthetic data generation, there are still limitations in diverse and efficient data synthesis. It is naive to expect that a simple combination of generative network-based data synthesis and data augmentation will solve these issues. Therefore, this paper proposes a novel data-free knowledge distillation method (SpaceshipNet) based on channel-wise feature exchange (CFE) and multi-scale spatial activation region consistency (mSARC) constraint. Specifically, CFE allows our generative network to better sample from the feature space and efficiently synthesize diverse images for learning the student network. However, using CFE alone can severely amplify the unwanted noises in the synthesized images, which may result in failure to improve distillation learning and even have negative effects. Therefore, we propose mSARC to assure the student network can imitate not only the logit output but also the spatial activation region of the teacher network in order to alleviate the influence of unwanted noises in diverse synthetic images on distillation learning. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet, Imagenette, and ImageNet100 show that our method can work well with different backbone networks, and outperform the state-of-the-art DFKD methods. Code will be available at: https://github.com/skgyu/SpaceshipNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Data-Free_Knowledge_Distillation_via_Feature_Exchange_and_Activation_Region_Constraint_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Data-Free_Knowledge_Distillation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Data-Free_Knowledge_Distillation_via_Feature_Exchange_and_Activation_Region_Constraint_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Data-Free_Knowledge_Distillation_via_Feature_Exchange_and_Activation_Region_Constraint_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes From Natural Language | Aditya Sanghi, Rao Fu, Vivian Liu, Karl D.D. Willis, Hooman Shayani, Amir H. Khasahmadi, Srinath Sridhar, Daniel Ritchie | Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor achieves this in a multi-resolution approach that first generates in a low-dimensional latent space and then upscales to a higher resolution for improved shape fidelity. For improved shape diversity, we use a discrete latent space which is modeled using a transformer conditioned on CLIP's image-text embedding space. We also present a novel variant of classifier-free guidance, which improves the accuracy-diversity trade-off. Finally, we perform extensive experiments demonstrating that CLIP-Sculptor outperforms state-of-the-art baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_of_High-Fidelity_and_Diverse_Shapes_From_Natural_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_of_High-Fidelity_and_Diverse_Shapes_From_Natural_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sanghi_CLIP-Sculptor_Zero-Shot_Generation_of_High-Fidelity_and_Diverse_Shapes_From_Natural_CVPR_2023_paper.html | CVPR 2023 | null |
Mask-Free Video Instance Segmentation | Lei Ke, Martin Danelljan, Henghui Ding, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu | The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks are tedious and expensive to annotate, limiting the scale and diversity of existing VIS datasets. In this work, we aim to remove the mask-annotation requirement. We propose MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state. We leverage the rich temporal mask consistency constraints in videos by introducing the Temporal KNN-patch Loss (TK-Loss), providing strong mask supervision without any labels. Our TK-Loss finds one-to-many matches across frames, through an efficient patch-matching step followed by a K-nearest neighbor selection. A consistency loss is then enforced on the found matches. Our mask-free objective is simple to implement, has no trainable parameters, is computationally efficient, yet outperforms baselines employing, e.g., state-of-the-art optical flow to enforce temporal mask consistency. We validate MaskFreeVIS on the YouTube-VIS 2019/2021, OVIS and BDD100K MOTS benchmarks. The results clearly demonstrate the efficacy of our method by drastically narrowing the gap between fully and weakly-supervised VIS performance. Our code and trained models are available at http://vis.xyz/pub/maskfreevis. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ke_Mask-Free_Video_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ke_Mask-Free_Video_Instance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15904 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_Mask-Free_Video_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_Mask-Free_Video_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Continual Detection Transformer for Incremental Object Detection | Yaoyao Liu, Bernt Schiele, Andrea Vedaldi, Christian Rupprecht | Incremental object detection (IOD) aims to train an object detector in phases, each with annotations for new object categories. As other incremental settings, IOD is subject to catastrophic forgetting, which is often addressed by techniques such as knowledge distillation (KD) and exemplar replay (ER). However, KD and ER do not work well if applied directly to state-of-the-art transformer-based object detectors such as Deformable DETR and UP-DETR. In this paper, we solve these issues by proposing a ContinuaL DEtection TRansformer (CL-DETR), a new method for transformer-based IOD which enables effective usage of KD and ER in this context. First, we introduce a Detector Knowledge Distillation (DKD) loss, focusing on the most informative and reliable predictions from old versions of the model, ignoring redundant background predictions, and ensuring compatibility with the available ground-truth labels. We also improve ER by proposing a calibration strategy to preserve the label distribution of the training set, therefore better matching training and testing statistics. We conduct extensive experiments on COCO 2017 and demonstrate that CL-DETR achieves state-of-the-art results in the IOD setting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Continual_Detection_Transformer_for_Incremental_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Continual_Detection_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03110 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Continual_Detection_Transformer_for_Incremental_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Continual_Detection_Transformer_for_Incremental_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms | Yu Wang, Yadong Li, Hongbin Wang | Weakly-supervised temporal action localization aims to detect action boundaries in untrimmed videos with only video-level annotations. Most existing schemes detect temporal regions that are most responsive to video-level classification, but they overlook the semantic consistency between frames. In this paper, we hypothesize that snippets with similar representations should be considered as the same action class despite the absence of supervision signals on each snippet. To this end, we devise a learnable dictionary where entries are the class centroids of the corresponding action categories. The representations of snippets identified as the same action category are induced to be close to the same class centroid, which guides the network to perceive the semantics of frames and avoid unreasonable localization. Besides, we propose a two-stream framework that integrates the attention mechanism and the multiple-instance learning strategy to extract fine-grained clues and salient features respectively. Their complementarity enables the model to refine temporal boundaries. Finally, the developed model is validated on the publicly available THUMOS-14 and ActivityNet-1.3 datasets, where substantial experiments and analyses demonstrate that our model achieves remarkable advances over existing methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Two-Stream_Networks_for_Weakly-Supervised_Temporal_Action_Localization_With_Semantic-Aware_Mechanisms_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Two-Stream_Networks_for_Weakly-Supervised_Temporal_Action_Localization_With_Semantic-Aware_Mechanisms_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Two-Stream_Networks_for_Weakly-Supervised_Temporal_Action_Localization_With_Semantic-Aware_Mechanisms_CVPR_2023_paper.html | CVPR 2023 | null |
HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint | Beitong Zhou, Jing Lu, Kerui Liu, Yunlu Xu, Zhanzhan Cheng, Yi Niu | Recent developments of the application of Contrastive Learning in Semi-Supervised Learning (SSL) have demonstrated significant advancements, as a result of its exceptional ability to learn class-aware cluster representations and the full exploitation of massive unlabeled data. However, mismatched instance pairs caused by inaccurate pseudo labels would assign an unlabeled instance to the incorrect class in feature space, hence exacerbating SSL's renowned confirmation bias. To address this issue, we introduced a novel SSL approach, HyperMatch, which is a plug-in to several SSL designs enabling noise-tolerant utilization of unlabeled data. In particular, confidence predictions are combined with semantic similarities to generate a more objective class distribution, followed by a Gaussian Mixture Model to divide pseudo labels into a 'confident' and a 'less confident' subset. Then, we introduce Relaxed Contrastive Loss by assigning the 'less-confident' samples to a hyper-class, i.e. the union of top-K nearest classes, which effectively regularizes the interference of incorrect pseudo labels and even increases the probability of pulling a 'less confident' sample close to its true class. Experiments and in-depth studies demonstrate that HyperMatch delivers remarkable state-of-the-art performance, outperforming FixMatch on CIFAR100 with 400 and 2500 labeled samples by 11.86% and 4.88%, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_HyperMatch_Noise-Tolerant_Semi-Supervised_Learning_via_Relaxed_Contrastive_Constraint_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_HyperMatch_Noise-Tolerant_Semi-Supervised_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_HyperMatch_Noise-Tolerant_Semi-Supervised_Learning_via_Relaxed_Contrastive_Constraint_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_HyperMatch_Noise-Tolerant_Semi-Supervised_Learning_via_Relaxed_Contrastive_Constraint_CVPR_2023_paper.html | CVPR 2023 | null |
From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models | Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, Dacheng Tao, Steven Hoi | Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnection and task disconnection between LLM and VQA task. End-to-end training on vision and language data may bridge the disconnections, but is inflexible and computationally expensive. To address this issue, we propose Img2Prompt, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform zero-shot VQA tasks without end-to-end training. In order to provide such prompts, we further employ LLM-agnostic models to provide prompts that can describe image content and self-constructed question-answer pairs, which can effectively guide LLM to perform zero-shot VQA tasks. Img2Prompt offers the following benefits: 1) It can flexibly work with various LLMs to perform VQA. 2) Without the needing of end-to-end training, it significantly reduces the cost of deploying LLM for zero-shot VQA tasks. 3) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_From_Images_to_Textual_Prompts_Zero-Shot_Visual_Question_Answering_With_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_From_Images_to_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_From_Images_to_Textual_Prompts_Zero-Shot_Visual_Question_Answering_With_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_From_Images_to_Textual_Prompts_Zero-Shot_Visual_Question_Answering_With_CVPR_2023_paper.html | CVPR 2023 | null |
LEGO-Net: Learning Regular Rearrangements of Objects in Rooms | Qiuhong Anna Wei, Sijie Ding, Jeong Joon Park, Rahul Sajnani, Adrien Poulenard, Srinath Sridhar, Leonidas Guibas | Humans universally dislike the task of cleaning up a messy room. If machines were to help us with this task, they must understand human criteria for regular arrangements, such as several types of symmetry, co-linearity or co-circularity, spacing uniformity in linear or circular patterns, and further inter-object relationships that relate to style and functionality. Previous approaches for this task relied on human input to explicitly specify goal state, or synthesized scenes from scratch--but such methods do not address the rearrangement of existing messy scenes without providing a goal state. In this paper, we present LEGO-Net, a data-driven transformer-based iterative method for LEarning reGular rearrangement of Objects in messy rooms. LEGO-Net is partly inspired by diffusion models--it starts with an initial messy state and iteratively "de-noises" the position and orientation of objects to a regular state while reducing distance traveled. Given randomly perturbed object positions and orientations in an existing dataset of professionally-arranged scenes, our method is trained to recover a regular re-arrangement. Results demonstrate that our method is able to reliably rearrange room scenes and outperform other methods. We additionally propose a metric for evaluating regularity in room arrangements using number-theoretic machinery. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_LEGO-Net_Learning_Regular_Rearrangements_of_Objects_in_Rooms_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_LEGO-Net_Learning_Regular_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_LEGO-Net_Learning_Regular_Rearrangements_of_Objects_in_Rooms_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_LEGO-Net_Learning_Regular_Rearrangements_of_Objects_in_Rooms_CVPR_2023_paper.html | CVPR 2023 | null |
FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation | Junjie He, Pengyu Li, Yifeng Geng, Xuansong Xie | Recent attention in instance segmentation has focused on query-based models. Despite being non-maximum suppression (NMS)-free and end-to-end, the superiority of these models on high-accuracy real-time benchmarks has not been well demonstrated. In this paper, we show the strong potential of query-based models on efficient instance segmentation algorithm designs. We present FastInst, a simple, effective query-based framework for real-time instance segmentation. FastInst can execute at a real-time speed (i.e., 32.5 FPS) while yielding an AP of more than 40 (i.e., 40.5 AP) on COCO test-dev without bells and whistles. Specifically, FastInst follows the meta-architecture of recently introduced Mask2Former. Its key designs include instance activation-guided queries, dual-path update strategy, and ground truth mask-guided learning, which enable us to use lighter pixel decoders, fewer Transformer decoder layers, while achieving better performance. The experiments show that FastInst outperforms most state-of-the-art real-time counterparts, including strong fully convolutional baselines, in both speed and accuracy. Code can be found at https://github.com/junjiehe96/FastInst. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_FastInst_A_Simple_Query-Based_Model_for_Real-Time_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_FastInst_A_Simple_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08594 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_FastInst_A_Simple_Query-Based_Model_for_Real-Time_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_FastInst_A_Simple_Query-Based_Model_for_Real-Time_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking | Jinkun Cao, Jiangmiao Pang, Xinshuo Weng, Rawal Khirodkar, Kris Kitani | Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that objects move linearly. While this assumption is acceptable for very short periods of occlusion, linear estimates of motion for prolonged time can be highly inaccurate. Moreover, when there is no measurement available to update Kalman filter parameters, the standard convention is to trust the priori state estimations for posteriori update. This leads to the accumulation of errors during a period of occlusion. The error causes significant motion direction variance in practice. In this work, we show that a basic Kalman filter can still obtain state-of-the-art tracking performance if proper care is taken to fix the noise accumulated during occlusion. Instead of relying only on the linear state estimate (i.e., estimation-centric approach), we use object observations (i.e., the measurements by object detector) to compute a virtual trajectory over the occlusion period to fix the error accumulation of filter parameters. This allows more time steps to correct errors accumulated during occlusion. We name our method Observation-Centric SORT (OC-SORT). It remains Simple, Online, and Real-Time but improves robustness during occlusion and non-linear motion. Given off-the-shelf detections as input, OC-SORT runs at 700+ FPS on a single CPU. It achieves state-of-the-art on multiple datasets, including MOT17, MOT20, KITTI, head tracking, and especially DanceTrack where the object motion is highly non-linear. The code and models are available at https://github.com/noahcao/OC_SORT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Observation-Centric_SORT_Rethinking_SORT_for_Robust_Multi-Object_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Observation-Centric_SORT_Rethinking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2203.14360 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Observation-Centric_SORT_Rethinking_SORT_for_Robust_Multi-Object_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Observation-Centric_SORT_Rethinking_SORT_for_Robust_Multi-Object_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-View Azimuth Stereo via Tangent Space Consistency | Xu Cao, Hiroaki Santo, Fumio Okura, Yasuyuki Matsushita | We present a method for 3D reconstruction only using calibrated multi-view surface azimuth maps. Our method, multi-view azimuth stereo, is effective for textureless or specular surfaces, which are difficult for conventional multi-view stereo methods. We introduce the concept of tangent space consistency: Multi-view azimuth observations of a surface point should be lifted to the same tangent space. Leveraging this consistency, we recover the shape by optimizing a neural implicit surface representation. Our method harnesses the robust azimuth estimation capabilities of photometric stereo methods or polarization imaging while bypassing potentially complex zenith angle estimation. Experiments using azimuth maps from various sources validate the accurate shape recovery with our method, even without zenith angles. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Multi-View_Azimuth_Stereo_via_Tangent_Space_Consistency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Multi-View_Azimuth_Stereo_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16447 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Multi-View_Azimuth_Stereo_via_Tangent_Space_Consistency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Multi-View_Azimuth_Stereo_via_Tangent_Space_Consistency_CVPR_2023_paper.html | CVPR 2023 | null |
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