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检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"
5506 条 记 录,以下是571-580 订阅
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Class-wise Thresholding for Robust Out-of-Distribution Detection
Class-wise Thresholding for Robust Out-of-Distribution Detec...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Guarrera, Matteo Jin, Baihong Lin, Tung-Wei Zuluaga, Maria A. Chen, Yuxin Sangiovanni-Vincentelli, Alberto Univ Calif Berkeley Berkeley CA 94720 USA EURECOM Biot France Univ Chicago Chicago IL 60637 USA
We consider the problem of detecting Out-of-Distribution (OoD) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of several popular OoD detection methods a... 详细信息
来源: 评论
Recognising Team Activities from Noisy Data
Recognising Team Activities from Noisy Data
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26th IEEE conference on computer vision and pattern recognition (CVPR)
作者: Bialkowski, Alina Lucey, Patrick Carr, Peter Denman, Simon Matthews, Iain Sridharan, Sridha Disney Res Pittburgh PA 94565 USA Queensland Univ Technol Image & Video Lab Brisbane Qld 4072 Australia
Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable... 详细信息
来源: 评论
Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action recognition
Bio-inspired Dynamic 3D Discriminative Skeletal Features for...
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26th IEEE conference on computer vision and pattern recognition (CVPR)
作者: Chaudhry, Rizwan Ofli, Ferda Kurillo, Gregorij Bajcsy, Ruzena Vidal, Rene Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Univ Calif Berkeley Tele Immers Lah Berkeley CA 94720 USA
Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been propose... 详细信息
来源: 评论
On-Sensor Binarized Fully Convolutional Neural Network for Localisation and Coarse Segmentation
On-Sensor Binarized Fully Convolutional Neural Network for L...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Yanan Lu, Yao Shanghai Univ Sch Microelect Shanghai Peoples R China Univ Bristol Visual Informat Lab Bristol Avon England
Current neural networks are compatible with high-performance GPU/CPUs. However, implementing neural networks on emerging embedded sensor for inference is challenging due to sensor's unique hardware architecture an... 详细信息
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Difficulty Estimation with Action Scores for computer vision Tasks
Difficulty Estimation with Action Scores for Computer Vision...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Arriaga, Octavio Palacio, Sebastian Valdenegro-Toro, Matias Univ Bremen Bremen Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Univ Groningen Groningen Netherlands
As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficul... 详细信息
来源: 评论
Learning Discriminative Features with Class Encoder  29
Learning Discriminative Features with Class Encoder
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29th IEEE conference on computer vision and pattern recognition (CVPR)
作者: Shi, Hailin Zhu, Xiangyu Lei, Zhen Liao, Shengcai Li, Stan Z. Univ Chinese Acad Sci Chinese Acad Sci Ctr Biometr & Secur Res Beijing Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China
Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full... 详细信息
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Comparing Representations in Tracking for Event Camera-based SLAM
Comparing Representations in Tracking for Event Camera-based...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Jiao, Jianhao Huang, Huaiyang Li, Liang He, Zhijian Zhu, Yilong Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China
This paper investigates two typical image-type representations for event camera-based tracking: time surface (TS) and event map (EM). Based on the original TS-based tracker, we make use of these two representations... 详细信息
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SqueezeNeRF: Further factorized FastNeRF for memory-efficient inference
SqueezeNeRF: Further factorized FastNeRF for memory-efficien...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wadhwani, Krishna Kojima, Tamaki Sony Grp Corp Nihonbashi Tokyo Japan
Neural Radiance Fields (NeRF) has emerged as the state-of-the-art method for novel view generation of complex scenes, but is very slow during inference. Recently, there have been multiple works on speeding up NeRF inf... 详细信息
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SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
SeeTheSeams: Localized Detection of Seam Carving based Image...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Gudavalli, Chandrakanth Rosten, Erik Nataraj, Lakshmanan Chandrasekaran, Shivkumar Manjunath, B. S. Mayachitra Inc Santa Barbara CA 93111 USA UC Santa Barbara Elect & Comp Engn Dept Santa Barbara CA USA
Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or displace/remove roads in a satellite im... 详细信息
来源: 评论
Continual Learning Based on OOD Detection and Task Masking
Continual Learning Based on OOD Detection and Task Masking
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kim, Gyuhak Esmaeilpour, Sepideh Xiao, Changnan Liu, Bing Univ Illinois Chicago IL 60607 USA ByteDance Beijing Peoples R China
Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test... 详细信息
来源: 评论