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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"
22907 条 记 录,以下是4911-4920 订阅
排序:
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
Self-Supervised Learning for Semi-Supervised Temporal Action...
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Xiang Zhang, Shiwei Qing, Zhiwu Shao, Yuanjie Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China
Self-supervised learning presents a remarkable performance to utilize unlabeled data for various video tasks. In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action ... 详细信息
来源: 评论
DualGraph: A graph-based method for reasoning about label noise
DualGraph: A graph-based method for reasoning about label no...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, HaiYang Xing, XiMing Liu, Liang Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China
Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based m... 详细信息
来源: 评论
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening
RobustNet: Improving Domain Generalization in Urban-Scene Se...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Choi, Sungha Jung, Sanghun Yun, Huiwon Kim, Joanne T. Kim, Seungryong Choo, Jaegul LG AI Res Seoul South Korea Korea Adv Inst Sci & Technol Daejeon South Korea Korea Univ Seoul South Korea Sogang Univ Seoul South Korea
Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To address this issue, this paper proposes a ... 详细信息
来源: 评论
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid ...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang Yifan Wu, Shihao Oztireli, Cengiz Sorkine-Hornung, Olga Swiss Fed Inst Technol Zurich Switzerland Univ Cambridge Cambridge England
Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can encode a high quality surface with intricate details into the parameters of a deep neural network. However, o... 详细信息
来源: 评论
Self-generated Defocus Blur Detection via Dual Adversarial Discriminators
Self-generated Defocus Blur Detection via Dual Adversarial D...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhao, Wenda Shang, Cai Lu, Huchuan Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China
Although existing fully-supervised defocus blur detection (DBD) models significantly improve performance, training such deep models requires abundant pixel-level manual annotation, which is highly time-consuming and e... 详细信息
来源: 评论
Towards Evaluating and Training Verifiably Robust Neural Networks
Towards Evaluating and Training Verifiably Robust Neural Net...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lyu, Zhaoyang Guo, Minghao Wu, Tong Xu, Guodong Zhang, Kehuan Lin, Dahua SenseTime CUHK Joint Lab Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Ctr Perceptual & Interact Intelligence Hong Kong Peoples R China
Recent works have shown that interval bound propagation (!BP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP trained networks: CROWN, a bounding method... 详细信息
来源: 评论
RSG: A Simple but Effective Module for Learning Imbalanced Datasets
RSG: A Simple but Effective Module for Learning Imbalanced D...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Jianfeng Lukasiewicz, Thomas Hu, Xiaolin Cai, Jianfei Xu, Zhenghua Univ Oxford Oxford England Tsinghua Univ Beijing Peoples R China Monash Univ Melbourne Vic Australia Hebei Univ Technol Tianjin Peoples R China
Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG)... 详细信息
来源: 评论
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation
Generalizing to the Open World: Deep Visual Odometry with On...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Shunkai Wu, Xin Cao, Yingdian Zha, Hongbin Peking Univ Sch EECS Key Lab Machine Percept MOE PKU SenseTime Machine Vis Joint Lab Beijing Peoples R China
Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap between training and testing data make... 详细信息
来源: 评论
Neural Feature Search for RGB-Infrared Person Re-Identification
Neural Feature Search for RGB-Infrared Person Re-Identificat...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chen, Yehansen Wan, Lin Li, Zhihang Jing, Qianyan Sun, Zongyuan China Univ Geosci Sch Geog & Informat Engn Wuhan Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China
RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality retrieval problem, which aims at matching the person-of-interest over visible and infrared camera views. Most existing works achieve ... 详细信息
来源: 评论
LLaFS: When Large Language Models Meet Few-Shot Segmentation
LLaFS: When Large Language Models Meet Few-Shot Segmentation
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conference on computer vision and pattern recognition (CVPR)
作者: Lanyun Zhu Tianrun Chen Deyi Ji Jieping Ye Jun Liu Singapore University of Technology and Design Zhejiang University Alibaba Group
This paper proposes LLaFS, the first attempt to leverage large language models (LLMs) in few-shot segmentation. In contrast to the conventional few-shot segmentation methods that only rely on the limited and biased in... 详细信息
来源: 评论