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A Weakly Supervised Multi-task Ranking Framework for Actor–Action Semantic Segmentation.
- Source :
-
International Journal of Computer Vision . May2020, Vol. 128 Issue 5, p1414-1432. 19p. 2 Illustrations, 2 Diagrams, 3 Charts, 4 Graphs. - Publication Year :
- 2020
-
Abstract
- Modeling human behaviors and activity patterns has attracted significant research interest in recent years. In order to accurately model human behaviors, we need to perform fine-grained human activity understanding in videos. Fine-grained activity understanding in videos has attracted considerable recent attention with a shift from action classification to detailed actor and action understanding that provides compelling results for perceptual needs of cutting-edge autonomous systems. However, current methods for detailed understanding of actor and action have significant limitations: they require large amounts of finely labeled data, and they fail to capture any internal relationship among actors and actions. To address these issues, in this paper, we propose a novel Schatten p-norm robust multi-task ranking model for weakly-supervised actor–action segmentation where only video-level tags are given for training samples. Our model is able to share useful information among different actors and actions while learning a ranking matrix to select representative supervoxels for actors and actions respectively. Final segmentation results are generated by a conditional random field that considers various ranking scores for video parts. Extensive experimental results on both the actor–action dataset and the Youtube-objects dataset demonstrate that the proposed approach outperforms the state-of-the-art weakly supervised methods and performs as well as the top-performing fully supervised method. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MARKOV random fields
*HUMAN behavior models
*RANDOM fields
*KERNEL (Mathematics)
Subjects
Details
- Language :
- English
- ISSN :
- 09205691
- Volume :
- 128
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- International Journal of Computer Vision
- Publication Type :
- Academic Journal
- Accession number :
- 143074254
- Full Text :
- https://doi.org/10.1007/s11263-019-01244-7