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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11891 条记录，以下是1001-1010 订阅

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Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection

Model Barrier: A Compact Un-Transferable Isolation Domain fo...

引用

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

作者： Wang, Lianyu Wang, Meng Zhang, Daoqiang Fu, Huazhu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Agcy Sci Technol & Res STAR IHPC Singapore 138632 Singapore

ISBN: (纸本)9798350301298

As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners. Model IP protection involves preventing the use of well-trained models on unauthorized domains. To address this issue, we propose a novel approach called Compact Un-Transferable Isolation Domain (CUTI-domain), which acts as a barrier to block illegal transfers from authorized to unauthorized domains. Specifically, CUTI-domain blocks cross-domain transfers by highlighting the private style features of the authorized domain, leading to recognition failure on unauthorized domains with irrelevant private style features. Moreover, we provide two solutions for using CUTI-domain depending on whether the unauthorized domain is known or not: target-specified CUTI-domain and target-free CUTI-domain. Our comprehensive experimental results on four digit datasets, CIFAR10 & STL10, and VisDA-2017 dataset demonstrate that CUTI-domain can be easily implemented as a plug-and-play module with different backbones, providing an efficient solution for model IP protection.

关键词： accountability ethics in vision fairness privacy Transparency

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Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual recognition Problems

Learning Common Rationale to Improve Self-Supervised Represe...

引用

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

作者： Shu, Yangyang van den Hengel, Anton Liu, Lingqiao Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9798350301298

Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.(1)

关键词： Self-supervised or unsupervised representation learning

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OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution

OPE-SR: Orthogonal Position Encoding for Designing a Paramet...

引用

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

作者： Song, Gaochao Sun, Qian Zhang, Luo Su, Ran Shi, Jianfeng He, Ying Tianjin Univ Tianjin Peoples R China Nanjing Univ Informat Sci & Technol Nanjing Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Arbitrary-scale image super-resolution (SR) is often tackled using the implicit neural representation (INR) approach, which relies on a position encoding scheme to improve its representation ability. In this paper, we introduce orthogonal position encoding (OPE), an extension of position encoding, and an OPE-Upscale module to replace the INR-based upsampling module for arbitrary-scale image super-resolution. Our OPE-Upscale module takes 2D coordinates and latent code as inputs, just like INR, but does not require any training parameters. This parameter-free feature allows the OPE-Upscale module to directly perform linear combination operations, resulting in continuous image reconstruction and achieving arbitrary-scale image reconstruction. As a concise SR framework, our method is computationally efficient and consumes less memory than state-of-the-art methods, as confirmed by extensive experiments and evaluations. In addition, our method achieves comparable results with state-of-the-art methods in arbitrary-scale image super-resolution. Lastly, we show that OPE corresponds to a set of orthogonal basis, validating our design principle.

关键词： Low-level vision

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Prototypical Residual Networks for Anomaly Detection and Localization

Prototypical Residual Networks for Anomaly Detection and Loc...

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

作者： Zhang, Hui Wu, Zuxuan Wang, Zheng Chen, Zhineng Jiang, Yu-Gang Fudan Univ Shanghai Key Lab Intell Info Proc Sch CS Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Zhejiang Univ Technol Sch Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns;a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.

关键词： vision applications and systems

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ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

ShadowDiffusion: When Degradation Prior Meets Diffusion Mode...

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

作者： Guo, Lanqing Wang, Chong Yang, Wenhan Huang, Siyu Wang, Yufei Pfister, Hanspeter Wen, Bihan Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350301298

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset.(1)

关键词： Low-level vision

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Adaptive Channel Sparsity for Federated Learning under System Heterogeneity

Adaptive Channel Sparsity for Federated Learning under Syste...

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

作者： Liao, Dongping Gao, Xitong Zhao, Yiren Xu, Chengzhong Univ Macau State Key Lab IoTSC CIS Dept Taipa Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Imperial Coll London London England

ISBN: (纸本)9798350301298

Owing to the non-i.i.d. nature of client data, channel neurons in federated-learned models may specialize to distinct features for different clients. Yet, existing channel-sparse federated learning (FL) algorithms prescribe fixed sparsity strategies for client models, and may thus prevent clients from training channel neurons collaboratively. To minimize the impact of sparsity on FL convergence, we propose Flado to improve the alignment of client model update trajectories by tailoring the sparsities of individual neurons in each client. Empirical results show that while other sparse methods are surprisingly impactful to convergence, Flado can not only attain the highest task accuracies with unlimited budget across a range of datasets, but also significantly reduce the amount of floating-point operations (FLOPs) required for training more than by 10 x under the same communications budget, and push the Pareto frontier of communication/computation trade-off notably further than competing FL algorithms.

关键词： Efficient and scalable vision

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Recurrent Homography Estimation Using Homography-Guided Image Warping and Focus Transformer

Recurrent Homography Estimation Using Homography-Guided Imag...

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

作者： Cao, Si-Yuan Zhang, Runmin Luo, Lun Yu, Beinan Sheng, Zehua Li, Junwei Shen, Hui-Liang Zhejiang Univ Ningbo Innovat Ctr Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350301298

We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the attention-focusing mechanism in FocusFormer aggregates the intra-inter correspondence in a global -> nonlocal -> local manner. Thanks to the above strategies, RHWF ranks top in accuracy on a variety of datasets, including the challenging cross-resolution and cross-modal ones. Meanwhile, benefiting from the recurrent framework, RHWF achieves parameter efficiency despite the transformer architecture. Compared to previous state-of-the-art approaches LocalTrans and IHN, RHWF reduces the mean average corner error (MACE) by about 70% and 38.1% on the MSCOCO dataset, while saving the parameter costs by 86.5% and 24.6%. Similar to the previous works, RHWF can also be arranged in 1-scale for efficiency and 2-scale for accuracy, with the 1-scale RHWF already outperforming most of the previous methods. Source code is available at https://***/imdumpl78/RHWF.

关键词： Low-level vision

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Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection

Generating Features with Increased Crop-related Diversity fo...

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

作者： Xu, Jingyi Le, Hieu Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350301298

Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not contain the objects perfectly but overlap with them in many possible ways, exhibiting great variability in the difficulty levels of the proposals. Training a robust classifier against this crop-related variability requires abundant training data, which is not available in few-shot settings. To mitigate this issue, we propose a novel variational autoencoder (VAE) based data generation model, which is capable of generating data with increased crop-related diversity. The main idea is to transform the latent space such latent codes with different norms represent different crop-related variations. This allows us to generate features with increased crop-related diversity in difficulty levels by simply varying the latent norm. In particular, each latent code is rescaled such that its norm linearly correlates with the IoU score of the input crop w.r.t. the ground-truth box. Here the IoU score is a proxy that represents the difficulty level of the crop. We train this VAE model on base classes conditioned on the semantic code of each class and then use the trained model to generate features for novel classes. In our experiments our generated features consistently improve state-of-the-art few-shot object detection methods on the PASCAL VOC and MS COCO datasets.

关键词： detection recognition: Categorization retrieval

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Robust 3D Shape Classification via Non-local Graph Attention Network

Robust 3D Shape Classification via Non-local Graph Attention...

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

作者： Qin, Shengwei Li, Zhong Liu, Ligang Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou Peoples R China Huzhou Univ Sch Informat Huzhou Peoples R China Zhejiang Sci Tech Univ Sch Sci Hangzhou Zhejiang Peoples R China Univ Sci & Technol China Sch Math Sci Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

We introduce a non-local graph attention network (NL-GAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods.

关键词： detection recognition: Categorization retrieval

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Mask3D: Pre-training 2D vision Transformers by Learning Masked 3D Priors

Mask3D: Pre-training 2D Vision Transformers by Learning Mask...

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

作者： Hou, Ji Dai, Xiaoliang He, Zijian Dai, Angela Niessner, Matthias Meta Real Labs Menlo Pk CA 94025 USA Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Current popular backbones in computer vision, such as vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pre-training to embed these 3D priors into 2D learned feature representations. In contrast to traditional 3D contrastive learning paradigms requiring 3D reconstructions or multi-view correspondences, our approach is simple: we formulate a pre-text reconstruction task by masking RGB and depth patches in individual RGB-D frames. We demonstrate the Mask3D is particularly effective in embedding 3D priors into the powerful 2D ViT backbone, enabling improved representation learning for various scene understanding tasks, such as semantic segmentation, instance segmentation and object detection. Experiments show that Mask3D notably outperforms existing self-supervised 3D pre-training approaches on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image semantic segmentation.

关键词： 3D from single images

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