Mask Transfiner for High-Quality Instance Segmentation

• URL: http://arxiv.org/abs/2111.13673v1
• Date: Fri, 26 Nov 2021 18:58:22 GMT
• Title: Mask Transfiner for High-Quality Instance Segmentation
• Authors: Lei Ke, Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu
• Abstract summary: We present Mask Transfiner for high-quality and efficient instance segmentation. Our approach only processes detected error-prone tree nodes and self-corrects their errors in parallel. Our code and trained models will be available at http://vis.xyz/pub/transfiner.
• Score: 95.74244714914052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 will be available at http://vis.xyz/pub/transfiner.

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