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共 21180 条记录，以下是641-650 订阅

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Rethinking Feature-based Knowledge Distillation for Face recognition

Rethinking Feature-based Knowledge Distillation for Face Rec...

引用

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

作者： Li, Jingzhi Guo, Zidong Li, Hui Han, Seungju Baek, Ji-won Yang, Min Yang, Ran Suh, Sungjoo Samsung R&D Inst China Xian SRCX Xian Peoples R China Samsung Adv Inst Technol SAIT Suwon South Korea

ISBN: (纸本)9798350301298

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.

关键词： body gesture Humans: Face movement pose

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Multi-modal Gait recognition via Effective Spatial-Temporal Feature Fusion

Multi-modal Gait Recognition via Effective Spatial-Temporal ...

引用

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

作者： Cui, Yufeng Kang, Yimei Beihang Univ Coll Software Beijing Peoples R China

ISBN: (纸本)9798350301298

Gait recognition is a biometric technology that identifies people by their walking patterns. The silhouettes-based method and the skeletons-based method are the two most popular approaches. However, the silhouette data are easily affected by clothing occlusion, and the skeleton data lack body shape information. To obtain a more robust and comprehensive gait representation for recognition, we propose a transformer-based gait recognition framework called MMGaitFormer, which effectively fuses and aggregates the spatial-temporal information from the skeletons and silhouettes. Specifically, a Spatial Fusion Module (SFM) and a Temporal Fusion Module (TFM) are proposed for effective spatial-level and temporal-level feature fusion, respectively. The SFM performs fine-grained body parts spatial fusion and guides the alignment of each part of the silhouette and each joint of the skeleton through the attention mechanism. The TFM performs temporal modeling through Cycle Position Embedding (CPE) and fuses temporal information of two modalities. Experiments demonstrate that our MMGaitFormer achieves state-of-the-art performance on popular gait datasets. For the most challenging "CL" (i.e., walking in different clothes) condition in CASIAB, our method achieves a rank-1 accuracy of 94.8%, which outperforms the state-of-the-art single-modal methods by a large margin.

关键词： body gesture Humans: Face movement pose

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Physical-World Optical Adversarial Attacks on 3D Face recognition

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

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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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Directional Connectivity-based Segmentation of Medical Images

Directional Connectivity-based Segmentation of Medical Image...

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

作者： Yang, Ziyun Farsiu, Sina Duke Univ Durham NC 27706 USA

ISBN: (纸本)9798350301298

Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity-based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://***/Zyun-Y/DconnNet.

关键词： cell microscopy Medical and biological vision

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FFCV: Accelerating Training by Removing Data Bottlenecks

FFCV: Accelerating Training by Removing Data Bottlenecks

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

作者： Leclerc, Guillaume Ilyas, Andrew Engstrom, Logan Park, Sung Min Salman, Hadi Madry, Aleksander MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process. In particular, we combine techniques such as an efficient file storage format, caching, data pre-loading, asynchronous data transfer, and just-in-time compilation to (a) make data loading and transfer significantly more efficient, ensuring that GPUs can reach full utilization;and (b) offload as much data processing as possible to the CPU asynchronously, freeing GPU cycles for training. Using FFCV, we train ResNet-18 and ResNet-50 on the ImageNet dataset with a state-of-the-art tradeoff between accuracy and training time. For example, across the range of ResNet-50 models we test, we obtain the same accuracy as the best baselines in half the time. We demonstrate FFCV's performance, ease-of-use, extensibility, and ability to adapt to resource constraints through several case studies. Detailed installation instructions, documentation, and Slack support channel are available at https://***/.

关键词： Efficient and scalable vision

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

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

作者： Raghavan, Siddeshwar He, Jiangpeng Zhu, Fengqing Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online (1)

关键词： computer vision long-tailed image classification online continual learning

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Use Your Head: Improving Long-Tail Video recognition

Use Your Head: Improving Long-Tail Video Recognition

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

作者： Perrett, Toby Sinha, Saptarshi Burghardt, Tilo Mirmehdi, Majid Damen, Dima Univ Bristol Bristol Avon England

ISBN: (纸本)9798350301298

This paper presents an investigation into long-tail video recognition. We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties. Most critically, they lack few-shot classes in their tails. In response, we propose new video benchmarks that better assess long-tail recognition, by sampling subsets from two datasets: SSv2 and VideoLT. We then propose a method, Long-Tail Mixed Reconstruction (LMR), which reduces overfitting to instances from few-shot classes by reconstructing them as weighted combinations of samples from head classes. LMR then employs label mixing to learn robust decision boundaries. It achieves state-of-the-art average class accuracy on EPIC-KITCHENS and the proposed SSv2-LT and VideoLT-LT. Benchmarks and code at: ***/tobyperrett/lmr

关键词： Video: Action and event understanding

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A Perspective on Deep vision Performance with Standard Image and Video Codecs

A Perspective on Deep Vision Performance with Standard Image...

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

作者： Reich, Christoph Hahn, Oliver Cremers, Daniel Roth, Stefan Debnath, Biplob Tech Univ Darmstadt Darmstadt Germany Tech Univ Munich Munich Germany NEC Labs Amer Inc San Jose CA 95110 USA Hessian Ctr AI Hessian AI Darmstadt Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9798350365474

Resource-constrained hardware, such as edge devices or cell phones, often rely on cloud servers to provide the required computational resources for inference in deep vision models. However, transferring image and video data from an edge or mobile device to a cloud server requires coding to deal with network constraints. The use of standardized codecs, such as JPEG or H.264, is prevalent and required to ensure interoperability. This paper aims to examine the implications of employing standardized codecs within deep vision pipelines. We find that using JPEG and H.264 coding significantly deteriorates the accuracy across a broad range of vision tasks and models. For instance, strong compression rates reduce semantic segmentation accuracy by more than 80% in mIoU. In contrast to previous findings, our analysis extends beyond image and action classification to localization and dense prediction tasks, thus providing a more comprehensive perspective.

关键词： Image Classification Image Compression Object Detectio Optical Flow Estimation Semantic Segmentation Video Compression

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VLM-PL: Advanced Pseudo Labeling approach for Class Incremental Object Detection via vision-Language Model

VLM-PL: Advanced Pseudo Labeling approach for Class Incremen...

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

作者： Kim, Junsu Ku, Yunhoe Kim, Jihyeon Cha, Junuk Baek, Seungryul UNIST Ulsan South Korea MODULABS Seoul South Korea

ISBN: (纸本)9798350365474

In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially powerful, struggle with multi-scenario incremental learning due to their tendency to forget past knowledge. To overcome this, we introduce a new approach called vision-Language Model assisted Pseudo-Labeling (VLM-PL). This technique uses vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector. Then, we generate custom queries for each pseudo GT using carefully designed prompt templates that combine image and text features. This allows the VLM to classify the correctness through its responses. Furthermore, VLM-PL integrates refined pseudo and real GTs from upcoming training, effectively combining new and old knowledge. Extensive experiments conducted on the Pascal VOC and MS COCO datasets not only highlight VLM-PL's exceptional performance in multi-scenario but also illuminate its effectiveness in dual-scenario by achieving state-of-the-art results in both.

关键词： CIOD Class Incremental Object Detection Continual Learning Incremental Learning Object Detection Pseudo Labeling vision-Language Model

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Evaluating Confidence Calibration in Endoscopic Diagnosis Models

Evaluating Confidence Calibration in Endoscopic Diagnosis Mo...

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

作者： Dehghani, Nikoo Thijssen, Ayla van der Zander, Quirine E. W. Schreuder, Ramon-Michel Schoon, Erik J. van der Sommen, Fons de With, Peter H. N. Eindhoven Univ Technol Eindhoven Netherlands Maastricht Univ Med Ctr Maastricht Netherlands GROW Res Inst Oncol & Reprod Maastricht Netherlands Catharina Hosp Eindhoven Netherlands Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands

ISBN: (纸本)9798350365474

Colorectal polyps are prevalent precursors to colorectal cancer, making their accurate characterization essential for timely intervention and patient outcomes. Deep learning-based computer-aided diagnosis (CADx) systems have shown promising performance in the automated detection and categorization of colorectal polyps (CRP) using endoscopic images. However, alongside the advancement in diagnostic accuracy, the need for reliable and accurate quantification of uncertainty estimates within these systems has become increasingly important. The primary focus of this study is on refining the reliability of computer-aided diagnosis of CRPs within clinical practice. We perform an investigation of widely used model calibration techniques and how they translate into clinical applications, specifically for CRP categorization data. The experiments reveal that the Variational Inference method excels in intra-dataset calibration, but lacks efficiency and inter-dataset generalization. Laplace approximation and temperature scaling methods offer improved calibration across datasets.

关键词： Bayesian neural networks computer-aided diagnosis Confidence calibration Model reliability

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