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Viewpoint-Adaptive Representation Disentanglement Network for Change Captioning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 2620-2635
作者:  Tu, Yunbin;  Li, Liang;  Su, Li;  Du, Junping;  Lu, Ke;  Huang, Qingming
收藏  |  浏览/下载：7/0  |  提交时间：2023/12/04
Task analysis  Image coding  Adaptation models  Encoding  Computer science  Transformers  Semantics  Change captioning  representation disentanglement  viewpoint-adaptive  position-embedded representation learning  
Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2022, 页码: 27
作者:  Huang, Yukun;  Fu, Xueyang;  Li, Liang;  Zha, Zheng-Jun
收藏  |  浏览/下载：22/0  |  提交时间：2022/12/07
Person Re-ID  Representation learning  Vision in bad weather  Deep learning  Low-light image enhancement  
Graph Regularized Encoder-Decoder Networks for Image Representation Learning 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3124-3136
作者:  Yang, Shijie;  Li, Liang;  Wang, Shuhui;  Zhang, Weigang;  Huang, Qingming;  Tian, Qi
收藏  |  浏览/下载：36/0  |  提交时间：2021/12/01
Laplace equations  Visualization  Manifolds  Image reconstruction  Task analysis  Decoding  Semantics  Auto-encoder  encoder-decoder  graph regularizer  image representation learning  
Object Categorization Using Class-Specific Representations 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 9, 页码: 4528-4534
作者:  Zhang, Chunjie;  Cheng, Jian;  Li, Liang;  Li, Changsheng;  Tian, Qi
收藏  |  浏览/下载：51/0  |  提交时间：2019/12/10
Class-specific representation  image classification  object categorization  visual representation  
Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 卷号: 28, 期号: 7, 页码: 1550-1559
作者:  Zhang, Chunjie;  Liang, Chao;  Li, Liang;  Liu, Jing;  Huang, Qingming;  Tian, Qi
收藏  |  浏览/下载：54/0  |  提交时间：2019/12/12
Fine grained  image representation  semantic space  visual recognition