PointINS: Point-based Instance Segmentation
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A single-point feature has shown its effectiveness in object detection. However, for instance segmentation, it does not lead to satisfactory results. The reasons are two folds. Firstly, it has limited representation capacity. Secondly, it could be misaligned with potential instances. To address the above issues, we propose a new point-based framework, namely PointINS, to segment instances from single points. The core module of our framework is instance-aware convolution, including the instance-agnostic feature and instance-aware weights. Instance-agnostic feature for each Point-of-Interest (PoI) serves as a template for potential instance masks. In this way, instance-aware features are computed by convolving this template with instance-aware weights for following mask prediction. Given the independence of instance-aware convolution, PointINS is general and practical as a one-stage detector for anchor-based and anchor-free frameworks. In our extensive experiments, we show the effectiveness of our framework on RetinaNet and FCOS. With ResNet101 backbone, PointINS achieves 38.3 mask mAP on challenging COCO dataset, outperforming its competitors by a large margin. The code will be made publicly available.
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