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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"
3917 条 记 录,以下是161-170 订阅
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Color Invariant Skin Segmentation
Color Invariant Skin Segmentation
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Xu, Han Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA Virginia Tech Virginia Tech Transportat Inst Blacksburg VA 24061 USA
This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full ... 详细信息
来源: 评论
Learning Generalized Feature for Temporal Action Detection: Application for Natural Driving Action recognition Challenge
Learning Generalized Feature for Temporal Action Detection: ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Chuong Nguyen Ngoc Nguyen Su Huynh Vinh Nguyen Son Nguyen CyberCore AI Morioka Iwate Japan
This paper reports our approach for the 2022 AI City Challenge - Naturalistic Driving Action recognition (Track 3), where the objective is to detect when and what kinds of actions that a driver performs in a long, unt... 详细信息
来源: 评论
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
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
BigDetection: A Large-scale Benchmark for Improved Object De...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Cai, Likun Zhang, Zhi Zhu, Yi Zhang, Li Li, Mu Xue, Xiangyang Fudan Univ Shanghai Peoples R China Amazon Inc Seattle WA USA
Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark ter... 详细信息
来源: 评论
CSG0: Continual Urban Scene Generation with Zero Forgetting
CSG0: Continual Urban Scene Generation with Zero Forgetting
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Jain, Himalaya Tuan-Hung Vu Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France
With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in thi... 详细信息
来源: 评论
Can we trust bounding box annotations for object detection?
Can we trust bounding box annotations for object detection?
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Murrugarra-Llerena, Jeffri Kirsten, L. N. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil
Object detection is a classical problem in computer vision, and the vast majority of approaches require large annotated datasets for training and evaluation purposes. The most popular representations are bounding boxe... 详细信息
来源: 评论
Multiple Object Detection and Tracking in the Thermal Spectrum
Multiple Object Detection and Tracking in the Thermal Spectr...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: El Ahmar, Wassim A. Kolhatkar, Dhanvin Nowruzi, Farzan Erlik AlGhamdi, Hamzah Hou, Jonathan Laganiere, Robert Univ Ottawa Ottawa ON Canada Sensor Cortek Inc Ottawa ON Canada Pleora Technol Kanata ON Canada
Multiple Object Tracking (MOT) is an integral part of machine vision research. Most tracking-by-detection based MOT solutions utilize video streams from RGB cameras for their operation. However, for real-world applica... 详细信息
来源: 评论
Multi-Dimensional vision Transformer Compression via Dependency Guided Gaussian Process Search
Multi-Dimensional Vision Transformer Compression via Depende...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Hou, Zejiang Kung, Sun-Yuan Princeton Univ Princeton NJ 08544 USA
vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. Previous ViT pruning methods tend to prune the model along one dime... 详细信息
来源: 评论
Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors
Privacy-friendly Synthetic Data for the Development of Face ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Damer, Naser Lopez, Cesar Augusto Fontanillo Fang, Meiling Spiller, Noemie Pham, Minh Vu Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Katholieke Univ Leuven Ctr IT & IP Law Leuven Belgium
The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?". Towards that, this work introduces the first synthetic-ba... 详细信息
来源: 评论
Spacing Loss for Discovering Novel Categories
Spacing Loss for Discovering Novel Categories
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Joseph, K. J. Paul, Sujoy Aggarwal, Gaurav Biswas, Soma Rai, Piyush Han, Kai Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Google Res Bengaluru India Indian Inst Sci Bengaluru India Lndian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Hong Kong Hong Kong Peoples R China
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this ... 详细信息
来源: 评论