Most of the modern instance segmentation approaches fall into two categories: region-based approaches in which object bounding boxes are detected first and later used in cropping and segmenting instances; and keypoint-based approaches in which individual instances are represented by a set of keypoints followed by a dense pixel clustering around those keypoints. Despite the maturity of these two paradigms, we would like to report an alternative affinity-based paradigm where instances are segmented based on densely predicted affinities and graph partitioning algorithms. Such affinity-based approaches indicate that high-level graph features other than regions or keypoints can be directly applied in the instance segmentation task. In this work, we propose Deep Affinity Net, an effective affinity-based approach accompanied with a new graph partitioning algorithm Cascade-GAEC. Without bells and whistles, our end-to-end model results in 32.4% AP on Cityscapes val and 27.5% AP on test. It achieves the best single-shot result as well as the fastest running time among all affinity-based models. It also outperforms the region-based method Mask R-CNN.

本研究介绍了一种新的基于亲和力及图分割算法的实例分割方法，提出了Deep Affinity Net模型和Cascade-GAEC算法。实验结果表明，该方法在Cityscapes val和test数据集上分别取得了32.4%和27.5%的平均精度，且运行速度为同类亲和力方法中最快的，优于基于区域的方法Mask R-CNN。

深度亲和网络：通过亲和力进行实例分割