Prof. Zhang Shiqing has become the associate editor of IEEE Transactions on Affective Computing, a well-known international journal focusing on affective computing in the field of artificial intelligence (IF=11.2). Prof. Zhang is from the School of Electronics and Information Engineering of Taizhou University. He has received the notification of Prof. Jesse Hoey, who is the editor-in-chief of the journal from University of Waterloo, and will serve as the associate editor, responsible for the processing of manuscripts on the topics submitted to the journal.
In 2023, Prof. Zhang Shiqing and his team members published several high-quality papers in the field of affective computing:
(1) Shiqing Zhang, et al., Deep Learning-based Multimodal Emotion Recognition from Audio, Visual, and Text Modalities: a Systematic Review of Recent Advancements and Future Prospects. expert Systems with Applications, 121692, 2023. (SCI Region I TOP Journal, IF=8.5). This paper summarizes the status and progress of domestic and international research in the field of affective computing in the past decade.
Link to the paper:
https://www.sciencedirect.com/science/article/pii/S0957417423021942
(2) Shiqing Zhang, et al., MTDAN: A Lightweight Multi-scale Temporal Difference Attention Networks for Automated Video Depression Detection, IEEE Transactions on Affective Computing, Doi: 10.1109/TAFFC.2023.3312263, 2023. (CAS Region II, IF=11.2). This paper addresses the problem of large computational complexity for deep learning networks in general, and proposes a lightweight multi-scale temporal difference attention network for automatic detection of video depressive mood, as shown in the following figure.
Link to the paper:
https://ieeexplore.ieee.org/abstract/document/10262153
(3) Huiting Fan, Xingnan Zhang, Yingying Xu, Jiangxiong Fang, Shiqing Zhang (Corresponding author), Xiaoming Zhao, Jun Yu. Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals. 102161, 2023. (SCI Region I Top Journal, IF=18.6). The first author of this paper is Fan Huiting, a student studying for her master’s degree, and Prof. Zhang Shiqing is the first corresponding author. The paper addresses the problem of how to effectively fuse multimodal signals, and proposes a Transformer-based multimodal feature enhancement network for multimodal depressive mood detection, as shown in the following figure.
Link to the paper:
https://www.sciencedirect.com/
The papers above were funded by the National Natural Science Foundation of China (62276180) and the Key Project of Natural Science Foundation of Zhejiang Province (LZ20F020002).
It is learned that Prof. Zhang Shiqing chose affective computing as his main research topic during his on-the-job master’s degree study in 2005, and subsequently continued to be rooted in this field of research during his PhD and postdoctoral studies. By now, he has been working in the field of affective computing for 18 years, and has achieved a series of related innovative results, which have attracted much attention from domestic and international peers. He has published or released more than 70 papers in journals such as IEEE Transactions on Multimedia, IEEE Transactions on Affective Computing, IEEE Transactions on Circuits and Systems for Video Technology, Expert Systems with Applications, Information Fusion, ACM MM, and other important journals and conferences, among which there are 2 ESI highly cited papers. He has presided over 2 projects of National Natural Science Foundation of China in the direction of affective computing, 1 key project of Natural Science Foundation of Zhejiang Province, and 4 other provincial and ministerial projects.
