.png){kind=icon}
.png){kind=icon}
Return
A Weakly Supervised Multi-task Ranking Framework for Actor-Action Semantic Segmentation
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-019-01244-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Weakly supervised learning
Actor-action semantic segmentation
Multi-task ranking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.3
Papers:
3.9K
Citations:
2.8W
View more organizations