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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是141-150 订阅

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X-Pruner: eXplainable Pruning for vision Transformers

X-Pruner: eXplainable Pruning for Vision Transformers

引用

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

作者： Yu, Lu Xiang, Wei James Cook Univ Townsville Australia La Trobe Univ Melbourne Australia

ISBN: (纸本)9798350301298

Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requirements, making them impractical for deployment on edge platforms. Recent studies have proposed to prune transformers in an unexplainable manner, which overlook the relationship between internal units of the model and the target class, thereby leading to inferior performance. To alleviate this problem, we propose a novel explainable pruning framework dubbed X-Pruner, which is designed by considering the explainability of the pruning criterion. Specifically, to measure each prunable unit's contribution to predicting each target class, a novel explainability-aware mask is proposed and learned in an end-to-end manner. Then, to preserve the most informative units and learn the layer-wise pruning rate, we adaptively search the layer-wise threshold that differentiates between unpruned and pruned units based on their explainability-aware mask values. To verify and evaluate our method, we apply the X-Pruner on representative transformer models including the DeiT and Swin Transformer. Comprehensive simulation results demonstrate that the proposed X-Pruner outperforms the state-of-the-art black-box methods with significantly reduced computational costs and slight performance degradation. Code is available at https: //***/vickyyu90/XPruner.

关键词： Explainable computer vision

来源：评论

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PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification

PartMix: Regularization Strategy to Learn Part Discovery for...

引用

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

作者： Kim, Minsu Kim, Seungryong Park, Jungin Park, Seongheon Sohn, Kwanghoon Yonsei Univ Seoul South Korea Korea Univ Seoul South Korea Korea Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9798350301298

Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data augmentation technique tailored for the part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains unexplored. In this paper, we present a novel data augmentation technique, dubbed PartMix, that synthesizes the augmented samples by mixing the part descriptors across the modalities to improve the performance of part-based VI-ReID models. Especially, we synthesize the positive and negative samples within the same and across different identities and regularize the backbone model through contrastive learning. In addition, we also present an entropy-based mining strategy to weaken the adverse impact of unreliable positive and negative samples. When incorporated into existing part-based VI-ReID model, PartMix consistently boosts the performance. We conduct experiments to demonstrate the effectiveness of our PartMix over the existing VI-ReID methods and provide ablation studies.

关键词： detection recognition: Categorization retrieval

来源：评论

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CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search

CLIP2Protect: Protecting Facial Privacy using Text-Guided Ma...

引用

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

作者： Shamshad, Fahad Naseer, Muzammal Nandakumar, Karthik Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate "naturalistic" images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the given image from its latent code. This step produces a good initialization, aiding the generation of high-quality faces that resemble the given identity. Subsequently, user-defined makeup text prompts and identity-preserving regularization are used to guide the search for adversarial codes in the latent space. Extensive experiments demonstrate that faces generated by our approach have stronger black-box transferability with an absolute gain of 12.06% over the state-of-the-art facial privacy protection approach under the face verification task. Finally, we demonstrate the effectiveness of the proposed approach for commercial face recognition systems. Our code is available at https://***/fahadshamshad/Clip2Protect.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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Motion Information Propagation for Neural Video Compression

Motion Information Propagation for Neural Video Compression

引用

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

作者： Qi, Linfeng Li, Jiahao Li, Bin Li, Houqiang Lu, Yan Univ Sci & Technol China Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

In most existing neural video codecs, the information flow therein is uni-directional, where only motion coding provides motion vectors for frame coding. In this paper, we argue that, through information interactions, the synergy between motion coding and frame coding can be achieved. We effectively introduce bi-directional information interactions between motion coding and frame coding via our Motion Information Propagation. When generating the temporal contexts for frame coding, the high-dimension motion feature from the motion decoder serves as motion guidance to mitigate the alignment errors. Meanwhile, besides assisting frame coding at the current time step, the feature from context generation will be propagated as motion condition when coding the subsequent motion latent. Through the cycle of such interactions, feature propagation on motion coding is built, strengthening the capacity of exploiting long-range temporal correlation. In addition, we propose hybrid context generation to exploit the multi-scale context features and provide better motion condition. Experiments show that our method can achieve 12.9% bit rate saving over the previous SOTA neural video codec.

关键词： Low-level vision

来源：评论

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GIVL: Improving Geographical Inclusivity of vision-Language Models with Pre-Training Methods

GIVL: Improving Geographical Inclusivity of Vision-Language ...

引用

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

作者： Yin, Da Gao, Feng Thattai, Govind Johnston, Michael Chang, Kai -Wei Univ Calif Los Angeles Los Angeles CA 90095 USA Amazon Alexa AI Lexington MA USA

ISBN: (纸本)9798350301298

A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region. In fact, a significant proportion of knowledge is locally shared by people from certain regions but may not apply equally in other regions because of cultural differences. If a model is unaware of regional characteristics, it may lead to performance disparity across regions and result in bias against underrepresented groups. We propose GIVL, a Geographically Inclusive vision-and-Language Pre-trained model. There are two attributes of geo-diverse visual concepts which can help to learn geodiverse knowledge: 1) concepts under similar categories have unique knowledge and visual characteristics, 2) concepts with similar visual features may fall in completely different categories. Motivated by the attributes, we design new pre-training objectives Image-Knowledge Matching (IKM) and Image Edit Checking (IEC) to pre-train GIVL. Compared with similar-size models pre-trained with similar scale of data, GIVL achieves state-of-the-art (SOTA) and more balanced performance on geo-diverse V&L tasks.

关键词： language reasoning vision

来源：评论

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Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action recognition

Actionlet-Dependent Contrastive Learning for Unsupervised Sk...

引用

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

作者： Lin, Lilang Zhang, Jiahang Liu, Jiaying Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

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. Our project website is available at https://***/projects/ActCLR/

关键词： Video: Action and event understanding

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SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries

SplineCam: Exact Visualization and Characterization of Deep ...

引用

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

作者： Humayun, Ahmed Imtiaz Balestriero, Randall Ralakrishnan, Ouha Raraniuk, Richard Rice Univ Houston TX 77005 USA Meta AI FAIR New York NY USA

ISBN: (纸本)9798350301298

Current Deep Network (DN) visualization and interpretability methods rely heavily on data space visualizations such as scoring which dimensions of the data are responsible for their associated prediction or generating new data features or samples that best match a given DN unit or representation. In this paper, we go one step further by developing the first provably exact method for computing the geometry of a DN's mapping - including its decision boundary - over a specified region of the data space. By leveraging the theory of Continuous Piece-Wise Linear (CPWL) spline DNs, SplineCam exactly computes a DN's geometry without resorting to approximations such as sampling or architecture simplification. SplineCam applies to any DN architecture based on CPWL activation nonlinearities, including (leaky) ReLU, absolute value, maxout, and max-pooling and can also be applied to regression DNs such as implicit neural representations. Beyond decision boundary visualization and characterization, SplineCam enables one to compare architectures, measure generalizability, and sample from the decision boundary on or off the data manifold. Project website: ***/splinecam.

关键词： computer vision theory

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Prompt-Guided Zero-Shot Anomaly Action recognition using Pretrained Deep Skeleton Features

Prompt-Guided Zero-Shot Anomaly Action Recognition using Pre...

引用

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

作者： Sato, Fumiaki Hachiuma, Ryo Sekii, Taiki Konica Minolta Inc Tokyo Japan

ISBN: (纸本)9798350301298

This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and simultaneously addresses three limitations in the conventional skeleton-based approaches: target domain-dependent DNN training, robustness against skeleton errors, and a lack of normal samples. We present a unified, user prompt-guided zero-shot learning framework using a target domain-independent skeleton feature extractor, which is pretrained on a large-scale action recognition dataset. Particularly, during the training phase using normal samples, the method models the distribution of skeleton features of the normal actions while freezing the weights of the DNNs and estimates the anomaly score using this distribution in the inference phase. Additionally, to increase robustness against skeleton errors, we introduce a DNN architecture inspired by a point cloud deep learning paradigm, which sparsely propagates the features between joints. Furthermore, to prevent the unobserved normal actions from being misidentified as abnormal actions, we incorporate a similarity score between the user prompt embeddings and skeleton features aligned in the common space into the anomaly score, which indirectly supplements normal actions. On two publicly available datasets, we conduct experiments to test the effectiveness of the proposed method with respect to abovementioned limitations.

关键词： Video: Action and event understanding

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Physical-World Optical Adversarial Attacks on 3D Face recognition

Physical-World Optical Adversarial Attacks on 3D Face Recogn...

引用

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

作者： Li, Yanjie Li, Yiquan Dai, Xuelong Guo, Songtao Xiao, Bin Hong Kong Polytech Univ Hong Kong Peoples R China Chongqing Univ Chongqing Peoples R China

ISBN: (纸本)9798350301298

The success rate of current adversarial attacks remains low on real-world 3D face recognition tasks because the 3D-printing attacks need to meet the requirement that the generated points should be adjacent to the surface, which limits the adversarial example' searching space. Additionally, they have not considered unpredictable head movements or the non-homogeneous nature of skin reflectance in the real world. To address the real-world challenges, we propose a novel structured-light attack against structured-light-based 3D face recognition. We incorporate the 3D reconstruction process and skin's reflectance in the optimization process to get the end-to-end attack and present 3D transform invariant loss and sensitivity maps to improve robustness. Our attack enables adversarial points to be placed in any position and is resilient to random head movements while maintaining the perturbation unnoticeable. Experiments show that our new method can attack point-cloud-based and depth-image-based 3D face recognition systems with a high success rate, using fewer perturbations than previous physical 3D adversarial attacks.

关键词： Adversarial attack and defense

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iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-training for Visual recognition

iCLIP: Bridging Image Classification and Contrastive Languag...

引用

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

作者： Wei, Yixuan Cao, Yue Zhang, Zheng Peng, Houwen Yao, Zhuliang Xie, Zhenda Hue, Han Guo, Baining Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naive multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and meanwhile surpasses CLIP by 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https: //***/weiyx16/iCLIP.

关键词： language reasoning vision

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