2021年9月25日
15:00-16:00
报告人:黄民烈 清华大学
Emotional Intelligence in Dialog Systems
16:00-17:00
报告人:鲁继文 清华大学
Deep Metric Learning for Visual Content Understanding
主持人
陶建华研究员
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1 Emotional Intelligence in Dialog Systems
Emotional intelligence refers to “the ability to monitor one's own and other people's emotions, to discriminate between different emotions and label them appropriately, and to use emotional information to guide thinking and behavior”, which has been viewed as one of the most important intelligent behaviors of human being. So, do today’s dialog systems possess emotional intelligence? Can dialog systems accomplish tasks like emotion comforting, expressing empathy, or even counselling? In this talk, the speaker will present his research and related works on this topic, namely, generating controllable emotions, empathetic dialogs, providing emotional support, and even generating professional counselling responses.
报告人简介
黄民烈博士,清华大学计算机科学与技术系长聘副教授,中文信息学会自然语言生成与智能写作专委会副主任,CCF学术工委主任助理。IEEE、CCF高级会员。他的研究领域为自然语言处理,特别是自然语言生成、对话系统、阅读理解等。曾获得中国人工智能学会吴文俊人工智能科技进步奖一等奖(排名第一),中文信息学会汉王青年创新奖,阿里巴巴创新合作研究奖,获得国家自然科学基金重点项目资助。多次获得国际主流会议的最佳论文或提名(IJCAI、ACL、SIGDIAL等)。研发对话系统平台ConvLab和ConvLab2,多次组织国内外有影响力的对话系统评测与竞赛(DSTC8,DSTC9),获得NTCIR 2017年组织的短文本对话生成评测冠军。担任顶级期刊TNNLS(SCI一区,影响因子>11)编委,顶级期刊TACL的执行编辑,顶级会议ACL 2021 Diversity&Inclusion联合主席,ACL 2021资深领域主席(SAC),EMNLP 2021研讨会联合主席,多次担任ACL/EMNLP的领域主席。
他的主页位于:
http://coai.cs.tsinghua.edu.cn/hml/
In this talk, I will overview the trend of deep metric learning techniques and discuss how they are employed to boost the performance of various visual content understanding tasks. Specifically, I will introduce some of our proposed deep metric learning methods including discriminative deep metric learning, deep localized metric learning, deep coupled metric learning, multi-manifold deep metric learning, deep transfer metric learning, deep adversarial metric learning, multi-view deep metric learning, and interpretable deep metric learning, which are developed for different application-specific visual content understanding tasks such as face recognition, person re-identification, object recognition, action recognition, visual tracking, image set classification, and visual search. Lastly, I will discuss some open problems in deep metric learning to show how to further develop more advanced deep metric learning methods in the future.
报告人简介
鲁继文,清华大学自动化系长聘副教授,国家杰出青年科学基金获得者,IAPR Fellow。主要研究方向为计算机视觉,在PAMI/IJCV/CVPR/ICCV/ECCV上发表论文110余篇,主持承担国家自然科学基金联合重点项目、优秀青年科学基金、国家重点研发计划课题等科研项目10余项,第一完成人获2020年中国电子学会自然科学一等奖。担任国际期刊Pattern Recognition Letters主编,IEEE T-IP/T-CSVT/T-BIOM编委,国际会议ICME 2022大会主席,FG2023/VCIP2022/ICME2020程序委员会主席,中国计算机学会计算机视觉专委会和中国人工智能学会模式识别专委会常务委员。指导/联合指导的研究生获中国人工智能学会优秀博士学位论文奖(2人次)、中国图象图形学学会优秀博士学位论文奖和中国电子学会优秀硕士学位论文奖。
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