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Masked Jigsaw Puzzle: A Versatile Position Embedding for vision Transformers

Masked Jigsaw Puzzle: A Versatile Position Embedding for Vis...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ren, Bin Liu, Yahui Song, Yue Bi, Wei Cucchiara, Rita Sebe, Nicu Wang, Wei Univ Pisa Pisa Italy Univ Trento Trento TN Italy Tencent AI Lab Shenzhen Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9798350301298

Position Embeddings (PEs), an arguably indispensable component in vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc. To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method. In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded. Meanwhile, for the non-occluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor. The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack;2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy;3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin. The source code and trained models are available at https://***/yhlleo/ MJP.

关键词： Deep learning architectures and techniques

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SimPoE: Simulated Character Control for 3D Human Pose Estimation

SimPoE: Simulated Character Control for 3D Human Pose Estima...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yuan, Ye Wei, Shih-En Simon, Tomas Kitani, Kris Saragih, Jason Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook Real Labs Redmond WA USA

ISBN: (纸本)9781665445092

Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility.

关键词： Solid modeling computer vision Three-dimensional displays Pose estimation Dynamics Kinematics Reinforcement learning

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Robust fine-tuning of zero-shot models

Robust fine-tuning of zero-shot models

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wortsman, Mitchell Ilharco, Gabriel Kim, Jong Wook Li, Mike Kornblith, Simon Roelofs, Rebecca Lopes, Raphael Gontijo Hajishirzi, Hannaneh Farhadi, Ali Namkoong, Hongseok Schmidt, Ludwig Univ Washington Seattle WA 98195 USA OpenAI San Francisco CA USA Columbia Univ New York NY USA Google Res Brain Team Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference.

关键词： computer vision Computational modeling computer network reliability Transfer learning Neural networks Robustness Data models

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Adaptive Pyramid Context Network for Semantic Segmentation 32

Adaptive Pyramid Context Network for Semantic Segmentation

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： He, Junjun Deng, Zhongying Zhou, Lei Wang, Yali Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable properties of context features in segmentation task. Specially, we find that Global-guided Local Affinity (GLA) can play a vital role in constructing effective context features, while this property has been largely ignored in previous works. Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet) for semantic segmentation. APCNet adaptively constructs multi-scale contextual representations with multiple well-designed Adaptive Context Modules (ACMs). Specifically, each ACM leverages a global image representation as a guidance to estimate the local affinity coefficients for each sub-region, and then calculates a context vector with these affinities. We empirically evaluate our APCNet on three semantic segmentation and scene parsing datasets, including PASCAL VOC 2012, Pascal-Context,and ADE20K dataset. Experimental results show that APCNet achieves state-of-the-art performance on all three benchmarks, and obtains a new record 84.2% on PASCAL VOC 2012 test set without MS COCO pre-trained and any post-processing.

关键词： Deep Learning Grouping and Shape Segmentation vision Applications and Systems

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Generalising Fine-Grained Sketch-Based Image Retrieval 32

Generalising Fine-Grained Sketch-Based Image Retrieval

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pang, Kaiyue Li, Ke Yang, Yongxin Zhang, Honggang Hospedales, Timothy M. Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford Surrey England Queen Mary Univ London London England Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781728132938

Fine-grained sketch-based image retrieval (FG-SBIR) addresses matching specific photo instance using free-hand sketch as a query modality. Existing models aim to learn an embedding space in which sketch and photo can be directly compared. While successful, they require instancelevel pairing within each coarse-grained category as annotated training data. Since the learned embedding space is domain-specific, these models do not generalise well across categories. This limits the practical applicability of FG-SBIR. In this paper, we identify cross-category generalisation for FG-SBIR as a domain generalisation problem, and propose the first solution. Our key contribution is a novel unsupervised learning approach to model a universal manifold of prototypical visual sketch traits. This manifold can then be used to paramaterise the learning of a sketch/photo representation. Model adaptation to novel categories then becomes automatic via embedding the novel sketch in the manifold and updating the representation and retrieval function accordingly. Experiments on the two largest FG-SBIR datasets, Sketchy and QMUL-Shoe-V2, demonstrate the efficacy of our approach in enabling cross-category generalisation of FG-SBIR.

关键词： Big Data Categorization Deep Learning Large Scale Methods recognition: Detection Retrieval vision Applications and Systems

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Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

Farewell to Mutual Information: Variational Distillation for...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tian, Xudong Zhang, Zhizhong Lin, Shaohui Qu, Yanyun Xie, Yuan Ma, Lizhuang East China Normal Univ Shanghai Peoples R China Xiamen Univ Xiamen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to viewchanges by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information.

关键词： Training Correlation Fitting Redundancy Estimation Robustness pattern recognition

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Improving OCR-based Image Captioning by Incorporating Geometrical Relationship

Improving OCR-based Image Captioning by Incorporating Geomet...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Jing Tang, Jinhui Yang, Mingkun Bai, Xiang Luo, Jiebo Nanjing Univ Sci & Technol Nanjing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

OCR-based image captioning aims to automatically describe images based on all the visual entities (both visual objects and scene text) in images. Compared with conventional image captioning, the reasoning of scene text is required for OCR-based image captioning since the generated descriptions often contain multiple OCR tokens. Existing methods attempt to achieve this goal via encoding the OCR tokens with rich visual and semantic representations. However, strong correlations between OCR tokens may not be established with such limited representations. In this paper, we propose to enhance the connections between OCR tokens from the viewpoint of exploiting the geometrical relationship. We comprehensively consider the height, width, distance, IoU and orientation relations between the OCR tokens for constructing the geometrical relationship. To integrate the learned relation as well as the visual and semantic representations into a unified framework, a Long Short-Term Memory plus Relation-aware pointer network (LSTM-R) architecture is presented in this paper. Under the guidance of the geometrical relationship between OCR tokens, our LSTM-R capitalizes on a newly-devised relation-aware pointer network to select OCR tokens from the scene text for OCR-based image captioning. Extensive experiments demonstrate the effectiveness of our LSTM-R. More remarkably, LSTM-R achieves state-of-the-art performance on TextCaps, with the CIDEr-D score being increased from 98.0% to 109.3%.

关键词： Training Visualization computer vision Correlation Computational modeling Semantics computer architecture

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Text-image Alignment for Diffusion-based Perception

Text-image Alignment for Diffusion-based Perception

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kondapanenil, Neehar Marksl, Markus Knott, Manuel Guimaraes, Rogerio Perona, Pietro CALTECH Pasadena CA 91125 USA Swiss Data Sci Ctr Empa ETH Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: ***/TADP/Code page: ***/damaggu/TADP

关键词： ADE20K Alignment Cityscapes Cross-Attention Cross-Domain Depth Estimation Diffusion Domain Shift Generative Models Model Personalization NYUv2 Object Detection Segmentation vision-Language Models

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HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging

HerosNet: Hyperspectral Explicable Reconstruction and Optima...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Xuanyu Zhang, Yongbing Xiong, Ruiqin Sun, Qilin Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Harbin Inst Technol Shenzhen Peoples R China Peking Univ Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral cameras that are either slow, expensive, or bulky, reconstructing hyperspectral images (HSIs) from a low-budget snapshot measurement has drawn wide attention. By mapping a truncated numerical optimization algorithm into a network with a fixed number of phases, recent deep unfolding networks (DUNs) for spectral snapshot compressive sensing (SCI) have achieved remarkable success. However, DUNs are far from reaching the scope of industrial applications limited by the lack of cross-phase feature interaction and adaptive parameter adjustment. In this paper, we propose a novel Hyperspectral Explicable Reconstruction and Optimal Sampling deep Network for SCI, dubbed HerosNet, which includes several phases under the ISTA-unfolding framework. Each phase can flexibly simulate the sensing matrix and contextually adjust the step size in the gradient descent step, and hierarchically fuse and interact the hidden states of previous phases to effectively recover current HSI frames in the proximal mapping step. Simultaneously, a hardware-friendly optimal binary mask is learned end-to-end to further improve the reconstruction performance. Finally, our HerosNet is validated to outperform the state-of-the-art methods on both simulation and real datasets by large margins. The source code is available at https://***/jianzhangcs/HerosNet.

关键词： Photography Deep learning Correlation Fuses Sensors pattern recognition Iterative methods

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Low-Rank Rescaled vision Transformer Fine-Tuning: A Residual Design Approach

Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dong, Wei Zhang, Xing Chen, Bihui Yang, Dawei Lin, Zhijun Yang, Qingsen Wang, Peng Yang, Yang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Xian Univ Architecture & Technol Coll Informat & Control Engn Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9798350353006

Parameter-efficient fine-tuning for pre-trained vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://***/zstarN70/***.

关键词： Low-Rank Adaptation Parameter-efficient fine-tuning Singular Value Decomposition

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