中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Exploring Regularizations with Face, Body and Image Cues for Group Cohesion Prediction
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Exploring Regularizations with Face, Body and Image Cues for Group Cohesion Prediction
Da Guo; KaiWang; Jianfei Yang; Kaipeng Zhang; Xiaojiang Peng; Yu Qiao
2019
Conference NameICMI ’19
Conference Date2019
Conference PlaceBoulder, CO, USA
AbstractThis paper presents our approach for the group cohesion prediction sub-challenge in the EmotiW 2019. The task is to predict group cohesiveness in images. We mainly explore several regularizations with three types of visual cues, namely face, body ,and global image. Our main contribution is twofold. First, we jointly train the group cohesion prediction task and group emotion recognition task using multi-task learning strategy with all visual cues. Second, we elaborately design two regularizations, namely a rank loss and a hourglass loss, where the former aims to give a margin between the distance of distant categories and near categories and the later to avoid centralization predictions with only MSE loss. With careful evaluations, we finally achieve the second place in this sub-challenge with MSE of 0.43821 on the testing set.
Department多媒体
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/15910
Collection数字所
Recommended Citation
GB/T 7714
Da Guo,KaiWang,Jianfei Yang,et al. Exploring Regularizations with Face, Body and Image Cues for Group Cohesion Prediction[C],2019.
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