This paper conducts a systematic study on the role of spatial and temporal attention mechanism in the video salient object detection (VSOD) task. We present a two-stage spatial-temporal attention network, named STA-Net, which makes two major contributions. In the first stage, we devise a Multi-Scale-Spatial-Attention (MSSA) module to reduce calculation cost on non-salient regions while exploiting multi-scale saliency information. Such a sliced attention method offers an individual way to efficiently exploit the high-level features of the network with an enlarged receptive field. The second stage is to propose a Pyramid-Saliency-Shift-Aware (PSSA) module, which puts emphasis on the importance of dynamic object information since it offers a valid shift cue to confirm salient object and capture temporal information. Such a temporal detection module is able to encourage precise salient region detection. Exhaustive experiments show that the proposed STA-Net is effective for video salient object detection task, and achieves compelling performance in comparison with state-of-the-art.

本文对时空注意力机制在视频显着目标检测（VSOD）任务中的作用进行了系统的研究。我们提出了一个名为STA-Net的两阶段时空关注网络，它做出了两个主要贡献。在第一阶段，我们设计了一个多尺度空间注意力（MSSA）模块，以在利用多尺度显着性信息的同时降低非显着区域的计算成本。这种切分注意方法提供了一种单独的方法，可以有效利用具有扩大的接收场的网络的高级功能。第二阶段是提出金字塔显着性转移意识（PSSA）模块，它强调动态对象信息的重要性，因为它提供了有效的提示来确认显着对象并捕获时间信息。这种时间检测模块能够促进精确的显着区域检测。详尽的实验表明，所提出的STA-Net对于视频显着目标检测任务是有效的，并且与最新技术相比具有令人信服的性能。