CASIA OpenIR

浏览/检索结果: 共20条,第1-10条 帮助

限定条件                    
已选(0)清除 条数/页:   排序方式:
TextFormer: A Query-based End-to-end Text Spotter with Mixed Supervision 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 4, 页码: 704-717
作者:  Yukun Zhai;   Xiaoqiang Zhang;   Xiameng Qin;   Sanyuan Zhao;  Xingping Dong;   Jianbing Shen
Adobe PDF(2312Kb)  |  收藏  |  浏览/下载:17/5  |  提交时间:2024/07/18
End-to-end text spotting  arbitrarily-shaped texts  transformer  mixed supervision  multitask modeling  
Vision Transformers with Hierarchical Attention 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 4, 页码: 670-683
作者:  Yun Liu;   Yu-Huan Wu;   Guolei Sun;    Le Zhang;  Ajad Chhatkuli;   Luc Van Gool
Adobe PDF(1358Kb)  |  收藏  |  浏览/下载:22/6  |  提交时间:2024/07/18
Vision transformer  hierarchical attention  global attention  local attention  scene understanding  
An Empirical Study on Google Research Football Multi-agent Scenarios 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 3, 页码: 549-570
作者:  Yan Song;  He Jiang;  Zheng Tian;  Haifeng Zhang;  Yingping Zhang;  Jiangcheng Zhu;  Zonghong Dai;  Weinan Zhang;  Jun Wang
Adobe PDF(24588Kb)  |  收藏  |  浏览/下载:59/16  |  提交时间:2024/05/23
Multi-agent reinforcement learning (RL), distributed RL system, population-based training, reward shaping, game theory  
Text Difficulty Study: Do Machines Behave the Same as Humans Regarding Text Difficulty? 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 283-293
作者:  Bowen Chen;  Xiao Ding;  Yi Zhao;  Bo Fu;  Tingmao Lin;  Bing Qin;  Ting Liu
Adobe PDF(1796Kb)  |  收藏  |  浏览/下载:56/11  |  提交时间:2024/04/23
Cognition inspired natural language processing, psycholinguistics, explainability, text difficulty, curriculum learning  
Stability and Generalization of Hypergraph Collaborative Networks 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 184-196
作者:  Michael K. Ng;  Hanrui Wu;  Andy Yip
Adobe PDF(1211Kb)  |  收藏  |  浏览/下载:36/11  |  提交时间:2024/04/23
Hypergraphs, vertices, hyperedges, collaborative networks, graph convolutional neural networks (CNNs), stability, generalization guarantees  
A Simple yet Effective Framework for Active Learning to Rank 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 169-183
作者:  Qingzhong Wang;  Haifang Li;  Haoyi Xiong;  Wen Wang;  Jiang Bian;  Yu Lu;  Shuaiqiang Wang;  Zhicong Cheng;  Dejing Dou;  Dawei Yin
Adobe PDF(2194Kb)  |  收藏  |  浏览/下载:70/26  |  提交时间:2024/04/23
Search, information retrieval, learning to rank, active learning, query by committee  
A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 153-168
作者:  Zefa Hu;  Ziyi Ni;  Jing Shi;  Shuang Xu;  Bo Xu
Adobe PDF(1525Kb)  |  收藏  |  浏览/下载:65/22  |  提交时间:2024/04/23
Medical dialogue understanding, information extraction, text generation, knowledge-enhanced prompt, low-resource setting, data augmentation  
Transformer: A General Framework from Machine Translation to Others 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 514-538
作者:  Yang Zhao;  Jiajun Zhang;  Chengqing Zong
Adobe PDF(1415Kb)  |  收藏  |  浏览/下载:54/15  |  提交时间:2024/04/23
Neural machine translation, Transformer, document neural machine translation (NMT), multimodal NMT, low-resource NMT  
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 483-513
作者:  Wei-Chien Wang;  Euijoon Ahn;  Dagan Feng;  Jinman Kim
Adobe PDF(2691Kb)  |  收藏  |  浏览/下载:67/20  |  提交时间:2024/04/23
Self-supervised learning (SSL), contrastive learning, deep learning, medical image analysis, computer vision  
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 447-482
作者:  Xiao Wang;  Guangyao Chen;  Guangwu Qian;  Pengcheng Gao;  Xiao-Yong Wei;  Yaowei Wang;  Yonghong Tian;  Wen Gao
Adobe PDF(3540Kb)  |  收藏  |  浏览/下载:73/16  |  提交时间:2024/04/23
Multi-modal (MM), pre-trained model (PTM), information fusion, representation learning, deep learning