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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"
21008 条 记 录,以下是321-330 订阅
排序:
GLID: Pre-training a Generalist Encoder-Decoder vision Model
GLID: Pre-training a Generalist Encoder-Decoder Vision Model
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Liu, Jihao Zheng, Jinliang Liu, Yu Li, Hongsheng CUHK MMLab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China CPII InnoHK Hong Kong Peoples R China Tsinghua Univ Inst AI Ind Res AIR Shanghai Peoples R China
This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approaches, e.g., Masked Autoencoder, have s... 详细信息
来源: 评论
EgoGen: An Egocentric Synthetic Data Generator
EgoGen: An Egocentric Synthetic Data Generator
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Li, Gen Zhao, Kaifeng Zhang, Siwei Lyu, Xiaozhong Dusmanu, Mihai Zhang, Yan Pollefeys, Marc Tang, Siyu Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA
Understanding the world in first-person view is fundamental in Augmented Reality (AR). This immersive perspective brings dramatic visual changes and unique challenges compared to third-person views. Synthetic data has... 详细信息
来源: 评论
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar ...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Bao, Chong Zhang, Yinda Li, Yuan Zhang, Xiyu Yang, Bangbang Bao, Hujun Pollefeys, Marc Zhang, Guofeng Cui, Zhaopeng Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Google Mountain View CA 94043 USA Swiss Fed Inst Technol Zurich Switzerland ByteDance Beijing Peoples R China
Recently, we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars. However, due to the diversity of frameworks, there is no practical method to support high-lev... 详细信息
来源: 评论
Lift3D: Zero-Shot Lifting of Any 2D vision Model to 3D
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Varma, Mukund T. Wang, Peihao Fan, Zhiwen Wang, Zhangyang Su, Hao Ramamoorthi, Ravi Univ Calif San Diego La Jolla CA 92093 USA Univ Texas Austin Austin TX USA
In recent years, there has been an explosion of 2D vision models for numerous tasks such as semantic segmentation, style transfer or scene editing, enabled by large-scale 2D image datasets. At the same time, there has... 详细信息
来源: 评论
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Gene...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Liu, Zhixuan Schaldenbrand, Peter Okogwu, Beverley-Claire Peng, Wenxuan Yun, Youngsik Hundt, Andrew Kim, Jihie Oh, Jean Carnegie Mellon Univ Pittsburgh PA 15213 USA Nanyang Technol Univ Singapore Singapore Dongguk Univ Seoul South Korea
Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are known to produce images with harmful st... 详细信息
来源: 评论
RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos
RGBD Objects in the Wild: Scaling Real-World 3D Object Learn...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Xia, Hongchi Fu, Yang Liu, Sifei Wang, Xiaolong Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Calif San Diego San Diego CA USA NVIDIA Santa Clara CA USA
We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing, the direct capture of the depth channel allows... 详细信息
来源: 评论
SignGraph: A Sign Sequence is Worth Graphs of Nodes
SignGraph: A Sign Sequence is Worth Graphs of Nodes
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Gan, Shiwei Yin, Yafeng Jiang, Zhiwei Wen, Hongkai Xie, Lei Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Warwick Dept Comp Sci Warwick England
Despite the recent success of sign language research, the widely adopted CNN-based backbones are mainly migrated from other computer vision tasks, in which the contours and texture of objects are crucial for identifyi... 详细信息
来源: 评论
MMA: Multi-Modal Adapter for vision-Language Models
MMA: Multi-Modal Adapter for Vision-Language Models
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Yang, Lingxiao Zhang, Ru-Yuan Wang, Yanchen Xie, Xiaohua Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Stanford Univ Stanford CA USA
Pre-trained vision-Language Models (VLMs) have served as excellent foundation models for transfer learning in diverse downstream tasks. However, tuning VLMs for few-shot generalization tasks faces a discrimination - g... 详细信息
来源: 评论
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversari...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Wang, Sibo Zhang, Jie Yuan, Zheng Shan, Shiguang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China
Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability, while they are also vulnerable to imp... 详细信息
来源: 评论
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification
Leveraging Cross-Modal Neighbor Representation for Improved ...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Yi, Chao Ren, Lu Zhan, De-Chuan Ye, Han-Jia Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China
CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However, without specific optimization for unimodal scenarios, its performance in single-modal... 详细信息
来源: 评论