# A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation Source: [A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:19:37.191Z Authors: Feng Luo, Bin-Bin Gao, Jiangpeng Yan, Xiu Li Published: 2021-06-18T16:37:28.000Z ## Abstract Boundary-based instance segmentation has drawn much attention since of its attractive efficiency. However, existing methods suffer from the difficulty in long-distance regression. In this paper, we propose a coarse-to-fine module to address the problem. Approximate boundary points are generated at the coarse stage and then features of these points are sampled and fed to a refined regressor for fine prediction. It is end-to-end trainable since differential sampling operation is well supported in the module. Furthermore, we design a holistic boundary-aware branch and introduce instance-agnostic supervision to assist regression. Equipped with ResNet-101, our approach achieves 31.7\\% mask AP on COCO dataset with single-scale training and testing, outperforming the baseline 1.3\\% mask AP with less than 1\\% additional parameters and GFLOPs. Experiments also show that our proposed method achieves competitive performance compared to existing boundary-based methods with a lightweight design and a simple pipeline. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()