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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
30983 条 记 录,以下是4721-4730 订阅
排序:
Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding
Incremental Transformer Structure Enhanced Image Inpainting ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Dong, Qiaole Cao, Chenjie Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China
Image inpainting has made significant advances in recent years. However, it is still challenging to recover corrupted images with both vivid textures and reasonable structures. Some specific methods only tackle regula... 详细信息
来源: 评论
Revisiting Superpixels for Active Learning in Semantic Segmentation with Realistic Annotation Costs
Revisiting Superpixels for Active Learning in Semantic Segme...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Cai, Lile Xu, Xun Liew, Jun Hao Foo, Chuan Sheng Inst Infocomm Res Singapore Singapore Natl Univ Singapore Singapore Singapore
State-of-the-art methods for semantic segmentation are based on deep neural networks that are known to be data-hungry. Region-based active learning has shown to be a promising method for reducing data annotation costs... 详细信息
来源: 评论
Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection
Sylph: A Hypernetwork Framework for Incremental Few-shot Obj...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yin, Li Perez-Rua, Juan M. Liang, Kevin J. Meta AI Menlo Pk CA 94025 USA
We study the challenging incremental few-shot object detection (iFSD) setting. Recently, hypernetwork-based approaches have been studied in the context of continuous and finetune-free iFSD with limited success. We tak... 详细信息
来源: 评论
Few-Shot Head Swapping in the Wild
Few-Shot Head Swapping in the Wild
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Shu, Changyong Wu, Hemao Zhou, Hang Liu, Jiaming Hong, Zhibin Ding, Changxing Han, Junyu Liu, Jingtuo Ding, Errui Wang, Jingdong Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China South China Univ Technol Guangzhou Peoples R China
The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios. While face swapping has drawn much attention, the task of head swapp... 详细信息
来源: 评论
RMT: Retentive Networks Meet vision Transformers
RMT: Retentive Networks Meet Vision Transformers
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Fan, Qihang Huang, Huaibo Chen, Mingrui Liu, Hongmin He, Ran Chinese Acad Sci Inst Automat MAIS & CRIPAC Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China
vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years. However, the core component of ViT, Self-Attention, lacks explicit spatial priors and bears a quadratic comput... 详细信息
来源: 评论
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
Details or Artifacts: A Locally Discriminative Learning Appr...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liang, Jie Zeng, Hui Zhang, Lei HongKong Polytech Univ Hong Kong Peoples R China OPPO Res Shenzhen Peoples R China
Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and... 详细信息
来源: 评论
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast
Weakly Supervised Semantic Segmentation by Pixel-to-Prototyp...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Du, Ye Fu, Zehua Liu, Qingjie Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China
Though image-level weakly supervised semantic segmentation (WSSS) has achieved great progress with Class Activation Maps (CAMs) as the cornerstone, the large supervision gap between classification and segmentation sti... 详细信息
来源: 评论
Efficient Image Super-Resolution with Collapsible Linear Blocks
Efficient Image Super-Resolution with Collapsible Linear Blo...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Li Li, Dong Tian, Lu Shan, Yi Adv Micro Devices Inc Beijing Peoples R China
In this paper, we propose a simple but effective architecture for fast and accurate single image super-resolution. Unlike other compact image super-resolution methods based on hand-crafted designs, we first apply coar... 详细信息
来源: 评论
ObjectFormer for Image Manipulation Detection and Localization
ObjectFormer for Image Manipulation Detection and Localizati...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Junke Wu, Zuxuan Chen, Jingjing Han, Xintong Shrivastava, Abhinav Lim, Ser-Nam Jiang, Yu-Gang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Huya Inc Guangzhou Peoples R China Univ Maryland College Pk MD 20742 USA Meta AI New York NY USA
Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to de... 详细信息
来源: 评论
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
Self-Supervised Learning for Semi-Supervised Temporal Action...
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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