Patch assembly for real-time instance segmentation

Yutao Xu, Hanli Wang, Jian Zhu

Abstract: The paradigm of sliding window is proven effective for the task of visual instance segmentation in many popular research works. However, it still suffers from the bottleneck of inference time. To accelerate existing instance segmentation approaches which are dense sliding window based, this work introduces a novel approach, called patch assembly, which can be integrated into bounding box detectors for segmentation without extra up-sampling computations. A well-designed detector named PAMask is proposed to verify the effectiveness of the proposed approach. Benefitting from the simple structure as well as a fusion of multiple representations, PAMask has the ability to run in real time while achieving competitive performances. Besides, another effective technique called Center-NMS is designed to reduce the number of boxes for intersection of union calculation, which can be fully parallelized on device and contributes 0.6% mAP improvement both in detection and segmentation for free.