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Human action recognition by multiple spatial clues network.
- Source :
-
Neurocomputing . Apr2022, Vol. 483, p10-21. 12p. - Publication Year :
- 2022
-
Abstract
- Human action can be recognized in still images since the whole image represents an action with some spatial clues, such as human poses, action-specific parts, and global surroundings. To represent the spatial clues, the recent methods require labor-intensive annotations to locate the human body and objects, which are computationally intensive. To eliminate strong supervision, a Multiple Spatial Clues Network (MSCNet) is proposed to represent the spatial clues with only image-level action label. Neither accurately manual annotated bounding boxes nor extra labeled datasets are required as additional supervision. First, the proposed MSCNet exploits spatial-attention module to generate spatial attention regions, and detects the spatial clues with minimal supervision. Then, spatial clues exploitation is proposed to utilize the learned spatial clues with three modules: the context module, body + context module and body + semantics module. Experiments on three benchmark datasets demonstrate the effectiveness of the proposed MSCNet. [ABSTRACT FROM AUTHOR]
- Subjects :
- *HUMAN behavior
*HUMAN body
*SUPERVISED learning
*DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 483
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 155655289
- Full Text :
- https://doi.org/10.1016/j.neucom.2022.01.091