In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations.
In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images.
Specifically, we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module.
This is the first work that explicitly emphasizes the challenge of saliency shift, i. e., the video salient object(s) may dynamically change.
Detection of salient objects in image and video is of great importance in many computer vision applications.
STCRF is our extension of CRF to the temporal domain and describes the relationships among neighboring regions both in a frame and over frames.