TMANet: Triple Multi-Scale Attention based Network with Boundary Association Loss for Superpixel Segmentation

Abstract

Superpixel segmentation with deep learning has been proposed in recent years and is widely employed to reduce the input image primitives for subsequent computer vision tasks. In this paper, we propose a Triple Multi-Scale Attention based Network (TMANet) for superpixel segmentation. First, aiming to extract more detailed context information, we design a Triple Multi-Scale Attention (TMA) to adapt the varying object scale and reduce inevitable redundant information in the encoder. Moreover, we observe that the dark parts with low probability in the association map generated by the TMANet are closer to the superpixel boundary. Therefore, we devise Boundary Association (BA) loss based on the association map to obtain fine boundaries and contours. Extensive experiments on public datasets show that TMANet outperforms the state-of-the-art methods to a certain extent. The application in saliency object detection of remote sensing also demonstrates the superiority of the proposed method.