306 citations
- The University of SydneyAU10 papers
- University of Science and Technology of ChinaCN8 papers
- Chinese Academy of SciencesCN6 papers
- JDSU (United States)US6 papers
- University of Chinese Academy of SciencesCN5 papers
- Beihang UniversityCN4 papers
- Tsinghua UniversityCN4 papers
- Association for Computing MachineryUS3 papers
- Baidu (China)CN3 papers
- City University of Hong KongHK3 papers
- Institute of Computing TechnologyCN3 papers
- National University of SingaporeSG3 papers
16 papers · 1 filter
RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations
Wenjing Yan, Shan Li, Chengtao Que +2
Much of the work on automatic facial expression recognition relies on databases containing a certain number of emotion classes and their exaggerated facial configurations (generall…
Modeling Topical Relevance for Multi-Turn Dialogue Generation
Hainan Zhang, Yanyan Lan, Liang Pang +3
Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect th…
Learning to Localize Actions from Moments
Fuchen Long, Ting Yao, Zhaofan Qiu +3
With the knowledge of action moments (i.e., trimmed video clips that each contains an action instance), humans could routinely localize an action temporally in an untrimmed video.…
Black Re-ID: A Head-shoulder Descriptor for the Challenging Problem of Person Re-Identification
Boqiang Xu, Lingxiao He, Xingyu Liao +3
Person re-identification (Re-ID) aims at retrieving an input person image from a set of images captured by multiple cameras. Although recent Re-ID methods have made great success,…
Semi-Siamese Training for Shallow Face Learning
Hang Du, Hailin Shi, Yuchi Liu +4
Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) fo…
Loss Function Search for Face Recognition
Xiaobo Wang, Shuo Wang, Cheng Chi +2
In face recognition, designing margin-based (e.g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.…