# The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation

arXiv: https://arxiv.org/abs/2011.13241 · HTML: https://mlsanity.com/paper/2011.13241/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Implicit Feature Refinement for Instance Segmentation](https://mlsanity.com/paper/2112.04709/) (2021)
- [BoundarySqueeze: Image Segmentation as Boundary Squeezing](https://mlsanity.com/paper/2105.11668/) (2021)
- [A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation](https://mlsanity.com/paper/2106.10213/) (2021)
- [SegT: A Novel Separated Edge-guidance Transformer Network for Polyp Segmentation](https://mlsanity.com/paper/2306.10773/) (2023)
- [Zero Pixel Directional Boundary by Vector Transform](https://mlsanity.com/paper/2203.08795/) (2022)
- [COSNet: A Novel Semantic Segmentation Network using Enhanced Boundaries in Cluttered Scenes](https://mlsanity.com/paper/2410.24139/) (2024)

## Abstract (arXiv)

Pursuing a more coherent scene understanding towards real-time vision applications, single-stage instance segmentation has recently gained popularity, achieving a simpler and more efficient design than its two-stage counterparts. Besides, its global mask representation often leads to superior accuracy to the two-stage Mask R-CNN which has been dominant thus far. Despite the promising advances in single-stage methods, finer delineation of instance boundaries still remains unexcavated. Indeed, boundary information provides a strong shape representation that can operate in synergy with the fully-convolutional mask features of the single-stage segmenter. In this work, we propose Boundary Basis based Instance Segmentation(B2Inst) to learn a global boundary representation that can complement existing global-mask-based methods that are often lacking high-frequency details. Besides, we devise a unified quality measure of both mask and boundary and introduce a network block that learns to score the per-instance predictions of itself. When applied to the strongest baselines in single-stage instance segmentation, our B2Inst leads to consistent improvements and accurately parse out the instance boundaries in a scene. Regardless of being single-stage or two-stage frameworks, we outperform the existing state-of-the-art methods on the COCO dataset with the same ResNet-50 and ResNet-101 backbones.