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2024-04-04

Special Issue on Advances in Deep Learning for Human-Centric Visual Understanding Submission Date: 2024-04-30 Our daily lives revolve around people. One of artificial intelligence's primary goals is to create intelligent machines that enable humans to accomplish more and to live better lives. This requires machines to comprehend people’s emotional and physical characteristics, behaviors, and daily activities, among other things. As a result, human-centric visual comprehension is a critical and long-standing area of research in computer vision and artificial intelligence. It has a plethora of critical applications in our society, including security and safety, health care, and human-machine interfaces. Recent advances in deep learning have led to efficient and effective tools for dealing with the variability and complexity inherent in real-world environments. While significant progress has been made, there is still a significant gap in order to address complex human-centric visual reasoning tasks (e.g., understanding human-object interaction, analyzing human body language) and new challenges (e.g., face forgery detection). Thus, now is an excellent time to refocus research efforts on more comprehensive and in-depth human-centric visual comprehension, and ultimately on socially intelligent machines.

We welcome submissions of high-quality papers that introduce significant new theories, methods, applications, and insights into a variety of human-centric perception, reasoning, and analysis tasks. Possible subjects include, but are not limited to:


Human semantic parsing/fashion recognition

Human pose/shape estimation

Human activity recognition and trajectory prediction

Face detection/facial landmark detection/deepfake detection

Pedestrian detection/tracking/recognition/retrieval/re-identification

Human-object/-human interaction understanding

Human gaze/facial/body behavior analysis

Human visual attention mechanisms

Human-centric image/video synthesis

New benchmark datasets and survey papers related to the aforementioned topics


Guest editors:

Wenguan Wang, PhDZhejiang University, Hangzhou, China

Si Liu, PhDBeihang University, Beijing, China

Xiaojun Chang, PhDUniversity of Technology Sydney, Sydney, Australia

David Crandall, PhDIndiana University, Bloomington, United States of America

Haibin Ling, PhDStony Brook University, Stony Brook, United States of America

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