Bi-Directional Seed Attention Network For Interactive Image Segmentation

In interactive segmentation, the role of seed information provided by the user is significant. A seed is a clue to ease the ambiguity of the problem by making the object segmentation task interactive. However, in most deep network-based works, seed information has been used as an additional channel for input images. In this paper, we propose a novel bi-directional attention module for more actively using seed information. The proposed bi-directional seed attention module (BSA) operates based on the feature map of the segmentation network and the input seed map. Through our attention module, the network feature map is affected by the seed map, while the feature also updates the seed information. As a result, our system concentrates on the seed information and more accurately derives the segmentation result required by the user. We have conducted validation experiments on the four standard benchmark datasets, including SBD, GrabCut, Berkeley, and DAVIS, and achieved the state-of-the-art results.