FUSION TARGET ATTENTION MASK GENERATION NETWORK FOR VIDEO SEGMENTATION

Video segmentation aims to segment target objects in a video sequence, which remains a challenge due to the motion and deformation of objects. In this paper, we propose a novel attention-driven hybrid encoder-decoder network that generates object segmentation by fully leveraging spatial and temporal information. Firstly, a multi-branch network is designed to learn feature representation from object appearance, location and motion. Secondly, a target attention module is proposed to further exploit context information from learned representation. In addition, a novel edge loss is designed which constraints the model to generate salient edge features and accurate segmentation. The proposed model has been evaluated over two widely used public benchmarks, and experiments demonstrate its superior robustness and effectiveness as compared with the state of the arts.