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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是681-690 订阅

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FFCV: Accelerating Training by Removing Data Bottlenecks

FFCV: Accelerating Training by Removing Data Bottlenecks

引用

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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ELSA: Exploiting Layer-wise N:M Sparsity for vision Transformer Acceleration

ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transfor...

引用

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

作者： Huang, Ning-Chi Chang, Chi-Chih Lin, Wei-Cheng Taka, Endri Marculescu, Diana Wu, Kai-Chiang Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350365474

N:M sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing N:M sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized N:M sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same number of parameters. In this work, to address the challenge of selecting suitable sparse configuration for ViTs on N:M sparsity-supporting accelerators, we propose ELSA, Exploiting Layer-wise N:M Sparsity for ViTs. Considering not only all N:M sparsity levels supported by a given accelerator but also the expected throughput improvement, our methodology can reap the benefits of accelerators supporting mixed sparsity by trading off negligible accuracy loss with both memory usage and inference time reduction for ViT models. For instance, our approach achieves a noteworthy 2.9x reduction in FLOPs to both Swin-B and DeiT-B with only a marginal degradation of accuracy on ImageNet. Our code is publicly available at https://***/ningchihuang/ ELSA.

关键词： Deep neural networks

来源：评论

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Towards Professional Level Crowd Annotation of Expert Domain Data

Towards Professional Level Crowd Annotation of Expert Domain...

引用

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

作者： Wang, Pei Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798350301298

Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed. It is a human-in-the-loop SSL method, where crowd-source workers act as filters of pseudo-labels, replacing the unreliable confidence thresholding used by state-of-the-art SSL methods. To enable annotation by non-experts, classes are specified implicitly, via positive and negative sets of examples and augmented with deliberative explanations, which highlight regions of class ambiguity. In this way, SSL-HF leverages the strong low-shot learning and confidence estimation ability of humans to create an intuitive but effective labeling experience. Experiments show that SSL-HF significantly outperforms various alternative approaches in several benchmarks.

关键词： Efficient and scalable vision

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Emotic Masked Autoencoder on Dual-views with Attention Fusion for Facial Expression recognition

Emotic Masked Autoencoder on Dual-views with Attention Fusio...

引用

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

作者： Xuan-Bach Nguyen Hoang-Thien Nguyen Thanh-Huy Nguyen Nhu-Tai Do Quang Vinh Dinh Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam Posts & Telecommun Inst Technol Ho Chi Minh City Vietnam Ho Chi Minh City Univ Educ Ho Chi Minh City Vietnam Univ Econ Ho Chi Minh City UEH Vietnam Ho Chi Minh City Vietnam Vietnamese German Univ Binh Duong Vietnam

ISBN: (纸本)9798350365474

Facial Expression recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. By utilizing low-level feature information from the ipsilateral view (auxiliary view) before learning the highlevel feature that emphasizes the shift in the human facial expression, our work seeks to provide a straightforward yet innovative way to improve the examined view (main view). We also suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model, focusing on pivotal local elements. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset, as observed in both training and validation contexts.

关键词： Self-supervised learning

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MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

MoFusion: A Framework for Denoising-Diffusion-based Motion S...

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

作者： Dabral, Rishabh Mughal, Muhammad Hamza Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350301298

Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can synthesise long, temporally plausible, and semantically accurate motions based on a range of conditioning contexts (such as music and text). We also present ways to introduce well-known kinematic losses for motion plausibility within the motion-diffusion framework through our scheduled weighting strategy. The learned latent space can be used for several interactive motion-editing applications like in-betweening, seed-conditioning, and text-based editing, thus, providing crucial abilities for virtual-character animation and robotics. Through comprehensive quantitative evaluations and a perceptual user study, we demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature. We urge the reader to watch our supplementary video at https://***/projects/MoFusion/.

关键词： language reasoning vision

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MetaCLUE: Towards Comprehensive Visual Metaphors Research

MetaCLUE: Towards Comprehensive Visual Metaphors Research

引用

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

作者： Akula, Arjun R. Driscoll, Brendan Narayana, Pradyumna Changpinyo, Soravit Jia, Zhiwei Damle, Suyash Pruthi, Garima Basu, Sugato Guibas, Leonidas Freeman, William T. Li, Yuanzhen Jampani, Varun Google Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creative ideas through nuanced relationships between abstract concepts such as feelings. While computer vision benchmarks and approaches predominantly focus on understanding and generating literal interpretations of images, metaphorical comprehension of images remains relatively unexplored. Towards this goal, we introduce MetaCLUE, a set of vision tasks on visual metaphor. We also collect high-quality and rich metaphor annotations (abstract objects, concepts, relationships along with their corresponding object boxes) as there do not exist any datasets that facilitate the evaluation of these tasks. We perform a comprehensive analysis of state-of-the-art models in vision and language based on our annotations, highlighting strengths and weaknesses of current approaches in visual metaphor classification, localization, understanding (retrieval, question answering, captioning) and generation (text-to-image synthesis) tasks. We hope this work provides a concrete step towards developing AI systems with human-like creative capabilities. Project page: https://***

关键词： and reasoning language vision

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Pseudo-label based unsupervised fine-tuning of a monocular 3D pose estimation model for sports motions

Pseudo-label based unsupervised fine-tuning of a monocular 3...

引用

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

作者： Suzuki, Tomohiro Tanaka, Ryota Takeda, Kazuya Fujii, Keisuke Nagoya Univ Nagoya Aichi Japan

ISBN: (纸本)9798350365474

Accurate motion capture is useful for sports motion analysis, but requires higher acquisition costs. Monocular or few camera multi-view pose estimation provides an accessible but less accurate alternative, especially for sports motion, due to training on datasets of daily activities. In addition, multi-view estimation is still costly due to camera calibration. Therefore, it is desirable to develop an accurate and cost-effective motion capture system for the daily training in sports. In this paper, we propose an accurate and convenient sports motion capture system based on unsupervised fine-tuning. The proposed system estimates 3D joint positions by multi-view estimation based on automatic calibration with the human body. These results are used as pseudo-labels for fine-tuning of the recent higher performance monocular 3D pose estimation model. Since the fine-tuning improves the model accuracy for sports motion, we can choose multi-view or monocular estimation depending on the situation. We evaluated the system using a running motion dataset and ASPset-510, and showed that fine-tuning improved the performance of monocular estimation to the same level as that of multi-view estimation for running motion. Our proposed system can be useful for the daily motion analysis in sports.

关键词： computer vision Pose estimation Running Sports

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Multimodal Attack Detection for Action recognition Models

Multimodal Attack Detection for Action Recognition Models

引用

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

作者： Mumcu, Furkan Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (纸本)9798350365474

Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show that action recognition models can be breached in many ways. Hence using these models in practice raises significant security concerns. However, there are very few works which focus on defending against or detecting attacks. In this work, we propose a novel universal detection method which is compatible with any action recognition model. In our extensive experiments, we show that our method consistently detects various attacks against different target models with high true positive rates while satisfying very low false positive rates. Tested against four state-of-the-art attacks targeting four action recognition models, the proposed detector achieves an average AUC of 0.911 over 16 test cases while the best performance achieved by the existing detectors is 0.645 average AUC. This 41.2% improvement is enabled by the robustness of the proposed detector to varying attack methods and target models. The lowest AUC achieved by our detector across the 16 test cases is 0.837 while the competing detector's performance drops as low as 0.211. We also show that the proposed detector is robust to varying attack strengths. In addition, we analyze our method's real-time performance with different hardware setups to demonstrate its potential as a practical defense mechanism.

关键词： Action recognition Models Adversarial machine learning attacks Attack detection

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Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration

Towards Building Self-Aware Object Detectors via Reliable Un...

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

作者： Oksuz, Kemal Joy, Tom Dokania, Puneet K. Five AI Ltd Cambridge England

ISBN: (纸本)9798350301298

The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality. In this work, we address these issues, and introduce the Self Aware Object Detection (SAOD) task, a unified testing framework which respects and adheres to the challenges that object detectors face in safety-critical environments such as autonomous driving. Specifically, the SAOD task requires an object detector to be: robust to domain shift;obtain reliable uncertainty estimates for the entire scene;and provide calibrated confidence scores for the detections. We extensively use our framework, which introduces novel metrics and large scale test datasets, to test numerous object detectors in two different use-cases, allowing us to highlight critical insights into their robustness performance. Finally, we introduce a simple baseline for the SAOD task, enabling researchers to benchmark future proposed methods and move towards robust object detectors which are fit for purpose. Code is available at: https://***/fiveai/saod.

关键词： detection recognition: Categorization retrieval

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Directional Connectivity-based Segmentation of Medical Images

Directional Connectivity-based Segmentation of Medical Image...

引用

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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