DISF: Dynamic Instance Segmentation with Semantic Features

Abstract

In this work, we propose a flexible and efficient instance segmentation framework, termed DISF (Dynamic Instance Segmentation with Semantic Features), which is a more novel two-stage instance segmentation framework. Firstly, we divide the image into multiple regions of the same size and directly classify the pixels in different regions, which converts the instance-level segmentation task into the pixel-level classification task within the region. We make full use of the location and size information of objects to distinguish different instances of the same category and obtain relatively coarse instance segmentation results. Secondly, we decouple the prediction of instance masks into convolution kernel prediction and instance features prediction. The instance masks are dynamically generated by convolution operations between the predicted convolution kernel and instance features. The segmentation results in this way do not contain redundant information. Thirdly, a parallel branch of semantic segmentation is added to refine instance segmentation results further. Semantic features provide global information about the image from a higher level. Semantic features and instance features are sent to the Features Fusion Module (FFM) to optimize the relatively coarse instance segmentation results generated in the previous stage. The experimental results reveal the promising potential of DISF in instance-level recognition.