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检索条件"任意字段=IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops"
8947 条 记 录,以下是221-230 订阅
排序:
SymDNN: Simple & Effective Adversarial Robustness for Embedded Systems
SymDNN: Simple & Effective Adversarial Robustness for Embedd...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Dey, Swarnava Dasgupta, Pallab Chakrabarti, Partha P. Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India
We propose SymDNN, a Deep Neural Network (DNN) inference scheme, to segment an input image into small patches, replace those patches with representative symbols, and use the reconstructed image for CNN inference. This... 详细信息
来源: 评论
Federated Learning-based Driver Activity recognition for Edge Devices
Federated Learning-based Driver Activity Recognition for Edg...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA
Video action recognition has been an active area of research for the past several years. However, the majority of research is concentrated on recognizing a diverse range of activities in distinct environments. On the ... 详细信息
来源: 评论
Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text recognition
Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text Rec...
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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025
作者: Huttner, Lukas Mayr, Martin Gorges, Thomas Wu, Fei Seuret, Mathias Maier, Andreas Christlein, Vincent Friedrich-Alexander Universität Erlangen-Nürnberg Pattern Recognition Lab Erlangen91058 Germany
The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computer vision. This growth comes with rising demands for computatio... 详细信息
来源: 评论
Disentangled Loss for Low-Bit Quantization-Aware Training
Disentangled Loss for Low-Bit Quantization-Aware Training
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Allenet, Thibault Briand, David Bichler, Olivier Sentieys, Olivier CEA LIST Saclay France Univ Rennes INRIA Rennes France
Quantization-Aware Training (QAT) has recently showed a lot of potential for low-bit settings in the context of image classification. Approaches based on QAT are using the Cross Entropy Loss function which is the refe... 详细信息
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Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
Edge-enhanced Feature Distillation Network for Efficient Sup...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Yan Nankai Univ Nankai Baidu Joint Lab Tianjin Peoples R China
With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices. However, most existing ... 详细信息
来源: 评论
Out-Of-Distribution Detection In Unsupervised Continual Learning
Out-Of-Distribution Detection In Unsupervised Continual Lear...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: He, Jiangpeng Zhu, Fengqing Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA
Unsupervised continual learning aims to learn new tasks incrementally without requiring human annotations. However, most existing methods, especially those targeted on image classification, only work in a simplified s... 详细信息
来源: 评论
Multi-Camera Vehicle Tracking System for AI City Challenge 2022
Multi-Camera Vehicle Tracking System for AI City Challenge 2...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Fei Wang, Zhen Nie, Ding Zhang, Shiyi Jiang, Xingqun Zhao, Xingxing Hu, Peng BOE Technol Grp Beijing Peoples R China
Multi-Target Multi-Camera tracking is a fundamental task for intelligent traffic systems. The track 1 of AI City Challenge 2022 aims at the city-scale multi-camera vehicle tracking task. In this paper we propose an ac... 详细信息
来源: 评论
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
Searching for Efficient Neural Architectures for On-Device M...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Akin, Berkin Gupta, Suyog Long, Yun Spiridonov, Anton Wang, Zhuo White, Marie Xu, Hao Zhou, Ping Zhou, Yanqi
On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these acc... 详细信息
来源: 评论
M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer
M2DAR: Multi-View Multi-Scale Driver Action Recognition with...
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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023
作者: Ma, Yunsheng Yuan, Liangqi Abdelraouf, Amr Han, Kyungtae Gupta, Rohit Li, Zihao Wang, Ziran Purdue University College of Engineering United States Toyota Motor North America InfoTech Labs United States
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view... 详细信息
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Multi-view Multi-label Canonical Correlation Analysis for Cross-modal Matching and Retrieval
Multi-view Multi-label Canonical Correlation Analysis for Cr...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Sanghavi, Rushil Verma, Yashaswi IIT Jodhpur Jodhpur Rajasthan India
In this paper, we address the problem of cross-modal retrieval in presence of multi-view and multi-label data. For this, we present Multi-view Multi-label Canonical Correlation Analysis (or MVMLCCA), which is a genera... 详细信息
来源: 评论