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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Multi-Type Self-Attention Guided Degraded Saliency Detection

February 1, 2023

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Authors

Ziqi Zhou

Tianjin University


Zheng Wang

Tianjin University


Huchuan Lu

Dalian University of Technology


Song Wang

University of South Carolina


Meijun Sun

Tianjin University


DOI:

10.1609/aaai.v34i07.7010


Abstract:

Existing saliency detection techniques are sensitive to image quality and perform poorly on degraded images. In this paper, we systematically analyze the current status of the research on detecting salient objects from degraded images and then propose a new multi-type self-attention network, namely MSANet, for degraded saliency detection. The main contributions include: 1) Applying attention transfer learning to promote semantic detail perception and internal feature mining of the target network on degraded images; 2) Developing a multi-type self-attention mechanism to achieve the weight recalculation of multi-scale features. By computing global and local attention scores, we obtain the weighted features of different scales, effectively suppress the interference of noise and redundant information, and achieve a more complete boundary extraction. The proposed MSANet converts low-quality inputs to high-quality saliency maps directly in an end-to-end fashion. Experiments on seven widely-used datasets show that our approach produces good performance on both clear and degraded images.

Topics: AAAI

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HOW TO CITE:

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun Multi-Type Self-Attention Guided Degraded Saliency Detection Proceedings of the AAAI Conference on Artificial Intelligence (2020) 13082-13089.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun Multi-Type Self-Attention Guided Degraded Saliency Detection AAAI 2020, 13082-13089.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun (2020). Multi-Type Self-Attention Guided Degraded Saliency Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 13082-13089.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun. Multi-Type Self-Attention Guided Degraded Saliency Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.13082-13089.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun. 2020. Multi-Type Self-Attention Guided Degraded Saliency Detection. "Proceedings of the AAAI Conference on Artificial Intelligence". 13082-13089.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun. (2020) "Multi-Type Self-Attention Guided Degraded Saliency Detection", Proceedings of the AAAI Conference on Artificial Intelligence, p.13082-13089

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun, "Multi-Type Self-Attention Guided Degraded Saliency Detection", AAAI, p.13082-13089, 2020.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun. "Multi-Type Self-Attention Guided Degraded Saliency Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.13082-13089.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun. "Multi-Type Self-Attention Guided Degraded Saliency Detection". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 13082-13089.

Ziqi Zhou||Zheng Wang||Huchuan Lu||Song Wang||Meijun Sun. Multi-Type Self-Attention Guided Degraded Saliency Detection. AAAI[Internet]. 2020[cited 2023]; 13082-13089.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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