Learning Deep Relations to Promote Saliency Detection

Changrui Chen, Xin Sun, Yang Hua, Junyu Dong, Hongwei Xv

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Though saliency detectors has made stunning progress recently. The performances of the state-of-the-art saliency detectors are not acceptable in some confusing areas, e.g., object boundary. We argue that the feature spatial independence should be one of the root cause. This paper explores the ubiquitous relations on the deep features to promote the existing saliency detectors efficiently. We establish the relation by maximizing the mutual information of the deep features of the same category via deep neural networks to break this independence. We introduce a threshold-constrained training pair construction strategy to ensure that we can accurately estimate the relations between different image parts in a selfsupervised way. The relation can be utilized to further excavate the salient areas and inhibit confusing backgrounds. The experiments demonstrate that our method can significantly boost the performance of the state-of-the-art saliency detectors on various benchmark datasets. Besides, our model is label-free and extremely efficient. The inference speed is 140 FPS on a single GTX1080 GPU.
Original languageEnglish
Title of host publication34th AAAI Conference on Artificial Intelligence (AAAI-20): Proceedings
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Publication statusPublished - 12 Feb 2020

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399


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