Reinforcement cutting-agent learning for video object segmentation

Junwei Han, Le Yang, Dingwen Zhang, Xiaojun Chang, Xiaodan Liang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

31 Citations (Scopus)

Abstract

Video object segmentation is a fundamental yet challenging task in computer vision community. In this paper, we formulate this problem as a Markov Decision Process, where agents are learned to segment object regions under a deep reinforcement learning framework. Essentially, learning agents for segmentation is nontrivial as segmentation is a nearly continuous decision-making process, where the number of the involved agents (pixels or superpixels) and action steps from the seed (super)pixels to the whole object mask might be incredibly huge. To overcome this difficulty, this paper simplifies the learning of segmentation agents to the learning of a cutting-agent, which only has a limited number of action units and can converge in just a few action steps. The basic assumption is that object segmentation mainly relies on the interaction between object regions and their context. Thus, with an optimal object (box) region and context (box) region, we can obtain the desirable segmentation mask through further inference. Based on this assumption, we establish a novel reinforcement cutting-agent learning framework, where the cutting-agent consists of a cutting-policy network and a cutting-execution network. The former learns policies for deciding optimal object-context box pair, while the latter executes the cutting function based on the inferred object-context box pair. With the collaborative interaction between the two networks, our method can achieve the outperforming VOS performance on two public benchmarks, which demonstrates the rationality of our assumption as well as the effectiveness of the proposed learning framework.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
EditorsDavid Forsyth, Ivan Laptev, Aude Oliva, Deva Ramanan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages9080-9089
Number of pages10
ISBN (Electronic)9781538664209
ISBN (Print)9781538664216
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2018 - Salt Lake City, United States of America
Duration: 19 Jun 201821 Jun 2018
http://cvpr2018.thecvf.com/

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1063-6919

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2018
Abbreviated titleCVPR 2018
CountryUnited States of America
CitySalt Lake City
Period19/06/1821/06/18
Internet address

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