However, they usually focus on the Leveraging our synthetic video data (150k video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus. We have witnessed a growing interest in video salient object detection (vsod) techniques in today’s computer vision applications.
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The ccnet consists of two. In contrast with temporal info In this study, we introduce the unified saliency transformer (unist) framework, which comprehensively utilizes the essential attributes of video saliency prediction and video.
Video salient object detection (vsod) aims at locating the most attractive objects presented in video sequences by exploiting spatial and temporal cues.
Previous methods based on 3dcnn, convlstm, or optical flow have achieved great success in video salient object detection (vsod).
