Multi-scale Salient Instance Segmentation based on Encoder-Decoder
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:1445-1460, 2021.
Salient instance segmentation refers to segmenting noticeable instance objects in images. In the face of multi-scale salient instances and overlapping instances, the existing salient instance segmentation methods have great limitations including inaccurate detection of large-scale instances, missing detection of small-scale instances, and wrong segmentation of overlapping instances. In order to solve these problems, a new multi-scale salient instance segmentation network (MSISNet) based on encoder-decoder is proposed. Firstly, a receptive field encoder (RFE) is designed to alleviate the problems of inaccurate detection of large-scale instances, missing detection of small-scale instances, and especially wrong segmentation of overlapping instances. Then, a pyramid decoder (PD) for the detection branch is designed to further alleviate the problem of inaccurate detection of large-scale instances and the difficulty in locating small-scale instances. Finally, a multi-stage decoder (MSD) is designed to improve the quality of the segmentation mask. Experiments on salient instance segmentation dataset Salient Instance Segmentation-1K (SIS-1K) have been conducted and the results show that the proposed method MSISNet is superior to the existing salient instance segmentation methods MSRNet and S4Net, and achieves better segmentation accuracy and speed.