Semantic Segmentation with Reverse Attention

Abstract

Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation. Traditionally, the convolutional classifiers are taught to learn the representative semantic features of labeled semantic objects. In this work, we propose a reverse attention network (RAN) architecture that trains the network to capture the opposite concept (i.e., what are not associated with a target class) as well. The RAN is a three-branch network that performs the direct, reverse and reverse-attention learning processes simultaneously. Extensive experiments are conducted to show the effectiveness of the RAN in semantic segmentation. Being built upon the DeepLabv2-LargeFOV, the RAN achieves the state-of-the-art mIoU score (48.1%) for the challenging PASCAL-Context dataset. Significant performance improvements are also observed for the PASCAL-VOC, Person-Part, NYUDv2 and ADE20K datasets.

accepted for oral presentation in BMVC 2017

Cited by in corpus (5)

• Camouflaged Object Segmentation with Distraction Mining
• Instance Embedding Transfer to Unsupervised Video Object Segmentation
• Learning To Pay Attention To Mistakes
• Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
• Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation