Abstract
In recent years, the task of segmenting foreground objects from background in a video, i.e. video object segmentation (VOS), has received considerable attention. In this paper, we propose a single end-to-end trainable deep neural network, convolutional gated recurrent Mask-RCNN, for tackling the semi-supervised VOS task. We take advantage of both the instance segmentation network (Mask-RCNN) and the visual memory module (Conv-GRU) to tackle the VOS task. The instance segmentation network predicts masks for instances, while the visual memory module learns to selectively propagate information for multiple instances simultaneously, which handles the appearance change, the variation of scale and pose and the occlusions between objects. After offline and online training under purely instance segmentation losses, our approach is able to achieve satisfactory results without any post-processing or synthetic video data augmentation. Experimental results on DAVIS 2016 dataset and DAVIS 2017 dataset have demonstrated the effectiveness of our method for video object segmentation task.
Original language | English |
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Title of host publication | Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 |
Publisher | IEEE |
Pages | 2739-2748 |
Number of pages | 10 |
ISBN (Electronic) | 9781728150239 |
DOIs | |
Publication status | Published - Oct 2019 |
Event | 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of Duration: 27 Oct 2019 → 2 Nov 2019 Conference number: 17 http://iccv2019.thecvf.com/ |
Conference
Conference | 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 |
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Abbreviated title | ICCV 2019 |
Country/Territory | Korea, Republic of |
City | Seoul |
Period | 27/10/19 → 2/11/19 |
Internet address |
Keywords
- 2021 OA procedure
- Instance segmentation
- Memory module
- Video object segmentation
- Deep learning