Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09245 (cs)
[Submitted on 10 Mar 2026]
Title:Towards Instance Segmentation with Polygon Detection Transformers
Authors:Jiacheng Sun, Jiaqi Lin, Wenlong Hu, Haoyang Li, Xinghong Zhou, Chenghai Mao, Yan Peng, Xiaomao Li
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Abstract:One of the bottlenecks for instance segmentation today lies in the conflicting requirements of high-resolution inputs and lightweight, real-time inference. To address this bottleneck, we present a Polygon Detection Transformer (Poly-DETR) to reformulate instance segmentation as sparse vertex regression via Polar Representation, thereby eliminating the reliance on dense pixel-wise mask prediction. Considering the box-to-polygon reference shift in Detection Transformers, we propose Polar Deformable Attention and Position-Aware Training Scheme to dynamically update supervision and focus attention on boundary cues. Compared with state-of-the-art polar-based methods, Poly-DETR achieves a 4.7 mAP improvement on MS COCO test-dev. Moreover, we construct a parallel mask-based counterpart to support a systematic comparison between polar and mask representations. Experimental results show that Poly-DETR is more lightweight in high-resolution scenarios, reducing memory consumption by almost half on Cityscapes dataset. Notably, on PanNuke (cell segmentation) and SpaceNet (building footprints) datasets, Poly-DETR surpasses its mask-based counterpart on all metrics, which validates its advantage on regular-shaped instances in domain-specific settings.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09245 [cs.CV] |
| (or arXiv:2603.09245v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09245
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View a PDF of the paper titled Towards Instance Segmentation with Polygon Detection Transformers, by Jiacheng Sun and 7 other authors
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