Video saliency detection with gated CNN and residual architecture

This paper proposes a novel gated recurrently end-to-end model of video salient object detection task to enhance both the performances and efficiency. A context aware aggregation module is first introduced for spatial features learning, these spatial features are then fused directly with a squeeze and excitation block. A two stage gated recurrent module is follow for temporal feature learning and shortcut connections between spatial and temporal features are add to alleviate the degradation problem. Our model has real-time speed of 25 fps on a single GPU which is similar to the approaches for static image salient object detection. We evaluate the proposed method on DAVIS and FBMS dataset. Experimental results show that comparing with other state-of-art saliency detectors, our method is more effective and efficient.