Salient object detection has been revolutionised by convolutional neural network (CNN) recently. However, it is hard to transfer the state-of-the-art still-image based saliency detectors to videos directly, owing to the neglect of temporal contexts between frames. In this study, the authors propose a flow-driven attention network (FDAN) to exploit motion information for video salient object detection. FDAN consists of an appearance feature extractor, a motion-guided attention module and a saliency map regression module. It extracts the appearance feature per frame, refines appearance feature with optical flow and infers the ultimate saliency map, respectively. Motion-guided attention module is the core of FDAN, which extracts motion information in the form of attention. This attention mechanism is a two-branch CNN, fusing optical flow and appearance features. In addition, a shortcut connection is applied to the attention multiplied feature map for noise suppression intensively. Experimental results show that the proposed method can achieve performance on par with the state-of-the-art method flow-guided recurrent neural encoder on challenging benchmarks of Densely Annotated Video Segmentation and Freiburg–Berkeley Motion Segmentation while being two times faster in detection.

中文翻译：

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流驱动注意力网络用于视频显着目标检测

最近，卷积神经网络（CNN）彻底改变了显着目标检测技术。但是，由于忽略了帧之间的时间上下文，因此很难将基于最新静态图像的显着性检测器直接转换为视频。在这项研究中，作者提出了一种流驱动注意力网络（FDAN），以利用运动信息进行视频显着物体检测。FDAN由外观特征提取器，运动引导注意模块和显着性图回归模块组成。它每帧提取外观特征，通过光流细化外观特征，并分别推断出最终显着图。运动引导注意力模块是FDAN的核心，它以注意力形式提取运动信息。这种关注机制是两个分支的CNN，融合光流和外观特征。此外，快捷方式连接应用于关注度倍增的特征图，以集中抑制噪声。实验结果表明，该方法在具有挑战性的密集注释视频分割和弗莱堡-伯克利运动分割基准上，可以达到与最新技术的流引导循环神经编码器相同的性能，并且检测速度快两倍。