Name
Abstract | ||
|---|---|---|
Two-stage and query-based instance segmentation methods have achieved remarkable results. However, their segmented masks are still very coarse. In this paper, we present Mask Transfiner for high-quality and efficient instance segmentation. Instead of operating on regular dense tensors, our Mask Transfiner decomposes and represents the image regions as a quadtree. Our transformer-based approach only processes detected error-prone tree nodes and self-corrects their errors in parallel. While these sparse pixels only constitute a small proportion of the total number, they are critical to the final mask quality. This allows Mask Transfiner to predict highly accurate instance masks, at a low computational cost. Extensive experiments demonstrate that Mask Transfiner outperforms current instance segmentation methods on three popular benchmarks, significantly improving both two-stage and query-based frameworks by a large margin of +3.0 mask AP on COCO and BDD100K, and +6.6 boundary AP on Cityscapes. Our code and trained models are available at https://github.com/SysCV/transfiner. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.00437 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Segmentation,grouping and shape analysis | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Ke | 1 | 15 | 2.58 |
| Danelljan Martin | 2 | 1344 | 49.35 |
| Xia Li | 3 | 0 | 0.68 |
| Yu-Wing Tai | 4 | 2028 | 92.75 |
| Chi-Keung Tang | 5 | 0 | 1.01 |
| Fisher Yu | 6 | 1280 | 50.27 |