Flow driven attention network for video salient object detection

Abstract

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.