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.

最近越来越多的研究人员关注视频对象分割，因为它是众多计算机视觉应用的重要组成部分。尽管许多算法推动了其发展，但仍然存在一些开放性挑战。需要高效和强大的管道来解决外观变化和视频对象分割中类似背景对象的干扰。本文提出了一种新的神经网络，它集成了基于时间注意力的外观模型和边界感知损失。外观模型融合了特征空间中第一帧、前一帧和当前帧的外观信息，这有助于所提出的方法学习有判别力和鲁棒性的目标表示，避免传统传播方案的漂移问题。而且，边界感知损失用于网络训练。配备了边界感知损失，所提出的方法实现了更准确的分割结果，边界清晰。将所提出的方法与流行的基准数据集上的几种最新算法进行了比较。综合实验表明，所提出的方法在高帧率下取得了良好的性能。