Abstract
We present an efficient method for the semi-supervised video object segmentation. Our method achieves accuracy competitive with state-of-the-art methods while running in a fraction of time compared to others. To this end, we propose a deep Siamese encoder-decoder network that is designed to take advantage of mask propagation and object detection while avoiding the weaknesses of both approaches. Our network, learned through a two-stage training process that exploits both synthetic and real data, works robustly without any online learning or post-processing. We validate our method on four benchmark sets that cover single and multiple object segmentation. On all the benchmark sets, our method shows comparable accuracy while having the order of magnitude faster runtime. We also provide extensive ablation and add-on studies to analyze and evaluate our framework.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
| Publisher | IEEE Computer Society |
| Pages | 7376-7385 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781538664209 |
| DOIs | |
| Publication status | Published - 2018 Dec 14 |
| Event | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States Duration: 2018 Jun 18 → 2018 Jun 22 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| ISSN (Print) | 1063-6919 |
Conference
| Conference | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
|---|---|
| Country | United States |
| City | Salt Lake City |
| Period | 18/6/18 → 18/6/22 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
Cite this
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Fast Video Object Segmentation by Reference-Guided Mask Propagation. / Oh, Seoung Wug; Lee, Joon Young; Sunkavalli, Kalyan; Kim, Seon Joo.
Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society, 2018. p. 7376-7385 8578868 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Fast Video Object Segmentation by Reference-Guided Mask Propagation
AU - Oh, Seoung Wug
AU - Lee, Joon Young
AU - Sunkavalli, Kalyan
AU - Kim, Seon Joo
PY - 2018/12/14
Y1 - 2018/12/14
N2 - We present an efficient method for the semi-supervised video object segmentation. Our method achieves accuracy competitive with state-of-the-art methods while running in a fraction of time compared to others. To this end, we propose a deep Siamese encoder-decoder network that is designed to take advantage of mask propagation and object detection while avoiding the weaknesses of both approaches. Our network, learned through a two-stage training process that exploits both synthetic and real data, works robustly without any online learning or post-processing. We validate our method on four benchmark sets that cover single and multiple object segmentation. On all the benchmark sets, our method shows comparable accuracy while having the order of magnitude faster runtime. We also provide extensive ablation and add-on studies to analyze and evaluate our framework.
AB - We present an efficient method for the semi-supervised video object segmentation. Our method achieves accuracy competitive with state-of-the-art methods while running in a fraction of time compared to others. To this end, we propose a deep Siamese encoder-decoder network that is designed to take advantage of mask propagation and object detection while avoiding the weaknesses of both approaches. Our network, learned through a two-stage training process that exploits both synthetic and real data, works robustly without any online learning or post-processing. We validate our method on four benchmark sets that cover single and multiple object segmentation. On all the benchmark sets, our method shows comparable accuracy while having the order of magnitude faster runtime. We also provide extensive ablation and add-on studies to analyze and evaluate our framework.
UR - http://www.scopus.com/inward/record.url?scp=85055453355&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055453355&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2018.00770
DO - 10.1109/CVPR.2018.00770
M3 - Conference contribution
AN - SCOPUS:85055453355
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 7376
EP - 7385
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
PB - IEEE Computer Society
ER -