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The Model May Fit You: User-Generalized Cross-Modal Retrieval 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 24, 页码: 2998-3012
作者:  Ma, Xinhong;  Yang, Xiaoshan;  Gao, Junyu;  Xu, Changsheng
Adobe PDF(6549Kb)  |  收藏  |  浏览/下载：255/48  |  提交时间：2022/06/17
cross-modal retrieval  domain generalization  meta-learning  
Emotion Knowledge Driven Video Highlight Detection 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3999-4013
作者:  Qi, Fan;  Yang, Xiaoshan;  Xu, Changsheng
收藏  |  浏览/下载：202/0  |  提交时间：2021/12/28
Visualization  Training data  Predictive models  Training  Semantics  Emotion recognition  Computational modeling  Deep ranking  knowledge graph  video highlight detection  
Adversarial Multimodal Network for Movie Story Question Answering 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 1744-1756
作者:  Yuan, Zhaoquan;  Sun, Siyuan;  Duan, Lixin;  Li, Changsheng;  Wu, Xiao;  Xu, Changsheng
收藏  |  浏览/下载：167/0  |  提交时间：2021/08/15
Knowledge discovery  Motion pictures  Visualization  Task analysis  Generators  Gallium nitride  Natural languages  Movie question answering  adversarial network  multimodal understanding  
Multimodal Disentangled Domain Adaption for Social Media Event Rumor Detection 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 4441-4454
作者:  Zhang, Huaiwen;  Qian, Shengsheng;  Fang, Quan;  Xu, Changsheng
收藏  |  浏览/下载：220/0  |  提交时间：2022/01/27
Social networking (online)  Feature extraction  Task analysis  Adaptation models  Writing  Visualization  Training  Disentanglement representation learning  domain adaptation  event rumor detection  social media  
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：312/62  |  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：321/44  |  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  
Metadata Connector: Exploiting Hashtag and Tag for Cross-OSN Event Search 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 510-523
作者:  Gao, Yuqi;  Sang, Jitao;  Fu, Chengpeng;  Wang, Zhengjia;  Ren, Tongwei;  Xu, Changsheng
收藏  |  浏览/下载：169/0  |  提交时间：2021/03/08
Twitter  Tagging  Videos  YouTube  Flickr  Games  Social media search  cross-OSN application  social multimedia  
Learning Dual-Pooling Graph Neural Networks for Few-Shot Video Classification 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 4285-4296
作者:  Hu, Yufan;  Gao, Junyu;  Xu, Changsheng
收藏  |  浏览/下载：145/0  |  提交时间：2021/12/28
Task analysis  Feature extraction  Training  Testing  Streaming media  Data models  Semantics  Few-shot learning  graph neural networks  video classification  
HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 2335-2346
作者:  Chen, Chaofan;  Qian, Shengsheng;  Fang, Quan;  Xu, Changsheng
收藏  |  浏览/下载：214/0  |  提交时间：2021/11/02
Three-dimensional displays  Feature extraction  Task analysis  Layout  Logic gates  Machine learning  Two dimensional displays  Point cloud segmentation  hierarchical graph pooling  gated graph attention network  
Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 2321-2334
作者:  Hu, Jun;  Qian, Shengsheng;  Fang, Quan;  Xu, Changsheng
收藏  |  浏览/下载：232/0  |  提交时间：2021/11/02
Visualization  Semantics  Knowledge discovery  Context modeling  Portable computers  Task analysis  Object detection  Question-answering  attention  multi-modal  social multimedia