Advances in Deep Learning for Human-Centric Visual Understanding
摘要截稿:
全文截稿: 2024-04-30
影响因子: 3.121
期刊难度:
CCF分类: B类
中科院JCR分区:
• 大类 : 计算机科学 - 3区
• 小类 : 计算机:人工智能 - 3区
• 小类 : 工程:电子与电气 - 3区
Overview
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