A temporal attention based appearance model for video object segmentation

More and more researchers have recently paid attention to video object segmentation because it is an important building block for numerous computer vision applications. Although many algorithms promote its development, there are still some open challenges. Efficient and robust pipelines are needed to address appearance changes and the distraction from similar background objects in the video object segmentation. This paper proposes a novel neural network that integrates a temporal attention based appearance model and a boundary-aware loss. The appearance model fuses the appearance information of the first frame, the previous frame, and the current frame in the feature space, which assists the proposed method to learn a discriminative and robust target representation and avoid the drift problem of traditional propagation schemes. Moreover, the boundary-aware loss is employed for network training. Equipped with the boundary-aware loss, the proposed method achieves more accurate segmentation results with clear boundaries. The proposed method is compared with several recent state-of-the-art algorithms on popular benchmark datasets. Comprehensive experiments show that the proposed method achieves favorable performance with a high frame rate.