Journal Information
Computer Vision and Image Understanding (CVIU)
Impact Factor:

Call For Papers
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.

Research Areas Include:

• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Recent Advances in Modeling, Methodology and Applications of Action Recognition and Detection
Submission Date: 2020-09-15

Action recognition and detection in untrimmed videos is a challenging task with the goal to not only recognize the category a video belongs to, but also infer the start and end times of action instances. Action recognition and detection has found applications in critical domains such as unmanned driving, medical robotics, sports analysis, and safety monitoring. There is still significant room for improvement, for example by applying weakly- and self-supervised learning techniques to reduce annotation costs, adversarial learning to improve model robustness, or incremental leaning for online action detection. This special issue will feature the most recent advances in modeling, methodology and applications for action recognition and detection. It targets both academic researchers and industrial practitioners from machine learning and computer vision communities. We encourage novel and advanced techniques of action recognition and detection. Topics should be related to action recognition and detection include, but are not limited to: Novel models and methodologies for action recognition and detection New neural architectures for video understanding Weakly supervised learning Self-supervised learning Self-paced learning Reinforcement learning Adversarial learning Graph-based learning Online/incremental learning Multi-label/multi-task learning Representation learning Spatio-temporal processing New applications, including human behavior analysis in shops, safety monitoring, service robotics, unmanned driving and other scenarios
Last updated by Dou Sun in 2020-06-25
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