Attention forest for semantic segmentation

Abstract

Semantic segmentation is a classical task in computer vision. In this paper, we target to address the low confidence regions which traditional CNN can not solve very well in semantic segmentation task. Depending on different characteristics of low confidence regions, an adaptive and robust attention mechanism is important to focus on the informative regions but ignore the noisy parts in the image. Intuitively, one attention map only is not sufficient to model the interaction between the low confidence regions and its surrounding patches. Thus, in this paper, we propose an Attention Forest structure, a novel and robust attention mechanism, to handle the low confidence regions. Each Attention Tree structure can capture more interactions between current patches with its adjacent regions. Experiments on PASCAL VOC 2012 Dataset validate the effectiveness of our proposed algorithm.