中文版 | English

在结果中检索

CASIA OpenIR
（本次检索基于用户作品认领结果）

浏览/检索结果: 共33条，第1-10条 帮助

限定条件            
已选(0)清除 条数/页：   排序方式：
TENET: Beyond Pseudo-Labeling for Semi-supervised Few-shot Learning 期刊论文
Machine Intelligence Research, 2023, 页码: 0
作者:  Ma CC(马成丞);  Dong WM(董未名);  Xu CS(徐常胜)
Adobe PDF(741Kb)  |  收藏  |  浏览/下载：138/31  |  提交时间：2024/01/29
Semi-supervised few-shot learning  few-shot learning  pseudo-labeling  linear regression  low-rank reconstruction  
Reducing Vision-Answer Biases for Multiple-Choice VQA 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 4621-4634
作者:  Zhang, Xi;  Zhang, Feifei;  Xu, Changsheng
Adobe PDF(2684Kb)  |  收藏  |  浏览/下载：82/1  |  提交时间：2023/11/17
Multiple-choice VQA  vision-answer bias  causal intervention  counterfactual interaction learning  
Recovering Generalization via Pre-training-like Knowledge Distillation for Out-of-Distribution Visual Question Answering 期刊论文
IEEE Transactions on Multimedia, 2023, 卷号: 26, 页码: 1-15
作者:  Song, Yaguang;  Yang, Xiaoshan;  Wang, Yaowei;  Xu, Changsheng
Adobe PDF(2397Kb)  |  收藏  |  浏览/下载：196/49  |  提交时间：2023/06/12
Multi-modal Foundation Model  Out-of-Distribution Generalization  Visual Question Answering  Knowledge Distillation  
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer 会议论文
, Online, 2022-3-1
作者:  Xu, Yifan;  Zhang, Zhijie;  Zhang, Mengdan;  Sheng, Kekai;  Li, Ke;  Dong, Weiming;  Zhang, Liqing;  Xu, Changsheng;  Sun, Xing
Adobe PDF(636Kb)  |  收藏  |  浏览/下载：284/69  |  提交时间：2022/06/14
GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation 会议论文
IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, JUN 16-20, 2019
作者:  Ma, Xinhong;  Zhang, Tianzhu;  Xu, Changsheng
Adobe PDF(7239Kb)  |  收藏  |  浏览/下载：222/46  |  提交时间：2022/06/14
Joint Expression Synthesis and Representation Learning for Facial Expression Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 3, 页码: 1681-1695
作者:  Zhang, Xi;  Zhang, Feifei;  Xu, Changsheng
Adobe PDF(4827Kb)  |  收藏  |  浏览/下载：260/1  |  提交时间：2022/06/06
Face recognition  Task analysis  Generative adversarial networks  Image synthesis  Image recognition  Faces  Training  Facial expression recognition  facial image synthesis  generative adversarial network  representation learning  
Transformers in computational visual media: A survey 期刊论文
Computational Visual Media, 2021, 卷号: 8, 期号: 1, 页码: 33-62
作者:  Xu,Yifan;  Wei,Huapeng;  Lin,Minxuan;  Deng,Yingying;  Sheng,Kekai;  Zhang,Mengdan;  Tang,Fan;  Dong,Weiming;  Huang,Feiyue;  Xu,Changsheng
Adobe PDF(5366Kb)  |  收藏  |  浏览/下载：319/44  |  提交时间：2021/12/28
visual transformer  computational visual media (CVM)  high-level vision  low-level vision  image generation  multi-modal learning  
Exploring the Representativity of Art Paintings 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 2794-2805
作者:  Deng, Yingying;  Tang, Fan;  Dong, Weiming;  Ma, Chongyang;  Huang, Feiyue;  Deussen, Oliver;  Xu, Changsheng
Adobe PDF(5313Kb)  |  收藏  |  浏览/下载：281/41  |  提交时间：2021/11/03
Painting  Art  Image color analysis  Feature extraction  Task analysis  Engineering profession  Electronic mail  Representativity  style enhancement  feature representation  artwork evaluation  
Unsupervised Video Summarization via Relation-Aware Assignment Learning 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3203-3214
作者:  Gao, Junyu;  Yang, Xiaoshan;  Zhang, Yingying;  Xu, Changsheng
Adobe PDF(3649Kb)  |  收藏  |  浏览/下载：342/67  |  提交时间：2021/11/03
Feature extraction  Training  Optimization  Semantics  Recurrent neural networks  Task analysis  Graph neural network  unsupervised learning  video summarization  
Learning Coarse-to-Fine Graph Neural Networks for Video-Text Retrieval 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 2386-2397
作者:  Wang, Wei;  Gao, Junyu;  Yang, Xiaoshan;  Xu, Changsheng
Adobe PDF(2165Kb)  |  收藏  |  浏览/下载：352/47  |  提交时间：2021/11/02
Feature extraction  Encoding  Task analysis  Semantics  Data models  Cognition  Focusing  Video-text retrieval  graph neural network  coarse-to-fine strategy