Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2019
Abstract
This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks. By elaborately annotating three popular video segmentation datasets (DAVIS, Youtube-Objects and SegTrack V2) with dynamic eye-tracking data in the UVOS setting, for the first time, we quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgements during dynamic, task-driven viewing. Such novel observations provide an in-depth insight into the underlying rationale behind UVOS. Inspired by these findings, we decouple UVOS into two sub-tasks: UVOS-driven Dynamic Visual Attention Prediction (DVAP) in spatiotemporal domain, and Attention-Guided Object Segmentation (AGOS) in spatial domain. Our UVOS solution enjoys three major merits: 1) modular training without using expensive video segmentation annotations, instead, using more affordable dynamic fixation data to train the initial video attention module and using existing fixation-segmentation paired static/image data to train the subsequent segmentation module; 2) comprehensive foreground understanding through multi-source learning; and 3) additional interpretability from the biologically-inspired and assessable attention. Experiments on popular benchmarks show that, even without using expensive video object mask annotations, our model achieves compelling performance in comparison with state-of-the-arts.
Keywords
Segmentation, Grouping and Shape, Image and Video Synthesis
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): June 15-20, Long Beach, CA: Proceedings
First Page
3059
Last Page
3069
ISBN
9781728132938
Identifier
10.1109/CVPR.2019.00318
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
WANG, Wenguan; SONG, Hongmei; ZHAO, Shuyang; SHEN, Jianbing; ZHAO, Sanyuan; HOI, Steven C. H.; and LING, Haibin.
Learning unsupervised video object segmentation through visual attention. (2019). 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): June 15-20, Long Beach, CA: Proceedings. 3059-3069.
Available at: https://ink.library.smu.edu.sg/sol_research/3162
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/CVPR.2019.00318