STA-Net: spatial-temporal attention network for video salient object detection

This paper conducts a systematic study on the role of spatial and temporal attention mechanism in the video salient object detection (VSOD) task. We present a two-stage spatial-temporal attention network, named STA-Net, which makes two major contributions. In the first stage, we devise a Multi-Scale...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2021-06, Vol.51 (6), p.3450-3459
Main Authors: Bi, Hong-Bo, Lu, Di, Zhu, Hui-Hui, Yang, Li-Na, Guan, Hua-Ping
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Machines
Manufacturing
Mechanical Engineering
Modules
Object recognition
Processes
Salience
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Summary:This paper conducts a systematic study on the role of spatial and temporal attention mechanism in the video salient object detection (VSOD) task. We present a two-stage spatial-temporal attention network, named STA-Net, which makes two major contributions. In the first stage, we devise a Multi-Scale-Spatial-Attention (MSSA) module to reduce calculation cost on non-salient regions while exploiting multi-scale saliency information. Such a sliced attention method offers an individual way to efficiently exploit the high-level features of the network with an enlarged receptive field. The second stage is to propose a Pyramid-Saliency-Shift-Aware (PSSA) module, which puts emphasis on the importance of dynamic object information since it offers a valid shift cue to confirm salient object and capture temporal information. Such a temporal detection module is able to encourage precise salient region detection. Exhaustive experiments show that the proposed STA-Net is effective for video salient object detection task, and achieves compelling performance in comparison with state-of-the-art.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-01961-4