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SSER: Semi-Supervised Emotion Recognition Based on Triplet Loss and Pseudo Label

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成果类型:
期刊论文
作者:
Lili Pan;Weizhi Shao*;Siyu Xiong;Qianhui Lei;Shiqi Huang;...
通讯作者:
Weizhi Shao
作者机构:
[Lili Pan; Weizhi Shao; Siyu Xiong; Qianhui Lei] College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410114, China
[Shiqi Huang; Eric Beckman] Chaplin School of Hospitality and Tourism Management, Florida International University, North Miami 33181, USA
[Qinghua Hu] School of Artificial Intelligence, Tianjin University, Tianjin 300072, China
通讯机构:
[Weizhi Shao] C
College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410114, China
语种:
英文
期刊:
Knowledge-Based Systems
ISSN:
0950-7051
年:
2024
页码:
111595
基金类别:
CRediT authorship contribution statement Lili Pan: Validation, Supervision, acquisition. Weizhi Shao: Writing – original draft, Methodology, Conceptualization. Siyu Xiong: Software, Investigation. Qianhui Lei: Formal analysis. Shiqi Huang: Data curation. Eric Beckman: Writing – review & editing, Visualization. Qinghua Hu: Supervision.
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
Recently, emotion recognition from facial expressions has achieved unprecedented accuracy with the development of deep learning. Despite this progress, most existing emotion recognition methods are supervised and thus require extensive annotation. This issue is particularly pronounced in continuous domain datasets where annotation costs are very high. Furthermore, discrete domain datasets containing specific poses are too uniform to reflect complex and actual emotions. Existing methods that employ classification loss pay little attention to image similarity, making it difficult to distinguish ...

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