In recently years, some useful unsupervised video object segmentation methods that emphasize the common information in videos have been proposed. Despite the effectiveness of these methods, they ignore the information from the shallow layers of the network and thus fail to segment the details of the objects. To address this problem, we propose a multi-attention network for unsupervised video object segmentation (MANet). Recent studies show that the deep layers of networks are sensitive to high-level semantic information but messy details, while it is opposite for shallow layers. From this insight, a multi-attention module is designed by taking into account the information from the shallow layers in addition to that from the deep layers. This module can distinguish the primary object and segment the details of the object effectively by enhancing the common information between video frames while combing the features from the shallow layers and the deep layers. Experimental results on the DAVIS-2016 and SegTrack v2 datasets show that our network outperforms the state-of-the-art methods.

中文翻译：

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用于无监督视频对象分割的多注意网络

近年来，已经提出了一些有用的无监督视频对象分割方法，它们强调视频中的公共信息。尽管这些方法有效，但它们忽略了来自网络浅层的信息，因此无法分割对象的细节。为了解决这个问题，我们提出了一种用于无监督视频对象分割的多注意网络（MANet）。最近的研究表明，网络的深层对高层语义信息敏感，但细节混乱，而浅层则相反。根据这种见解，除了来自深层的信息外，还考虑了来自浅层的信息，从而设计了多注意模块。该模块通过增强视频帧之间的公共信息，同时结合浅层和深层的特征，可以有效区分主要对象并分割对象的细节。在 DAVIS-2016 和 SegTrack v2 数据集上的实验结果表明，我们的网络优于最先进的方法。