Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence

• URL: http://arxiv.org/abs/2012.04404v2
• Date: Wed, 9 Dec 2020 03:22:46 GMT
• Title: Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence
• Authors: Siyue Yu, Bingfeng Zhang, Jimin Xiao, Eng Gee Lim
• Abstract summary: We propose a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations. Our method achieves a new state-of-the-art performance on six benchmarks.
• Score: 14.79639149658596
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sparse labels have been attracting much attention in recent years. However, the performance gap between weakly supervised and fully supervised salient object detection methods is huge, and most previous weakly supervised works adopt complex training methods with many bells and whistles. In this work, we propose a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations without pre/post-processing operations or extra supervision data. Since scribble labels fail to offer detailed salient regions, we propose a local coherence loss to propagate the labels to unlabeled regions based on image features and pixel distance, so as to predict integral salient regions with complete object structures. We design a saliency structure consistency loss as self-consistent mechanism to ensure consistent saliency maps are predicted with different scales of the same image as input, which could be viewed as a regularization technique to enhance the model generalization ability. Additionally, we design an aggregation module (AGGM) to better integrate high-level features, low-level features and global context information for the decoder to aggregate various information. Extensive experiments show that our method achieves a new state-of-the-art performance on six benchmarks (e.g. for the ECSSD dataset: F_\beta = 0.8995, E_\xi = 0.9079 and MAE = 0.0489$), with an average gain of 4.60\% for F-measure, 2.05\% for E-measure and 1.88\% for MAE over the previous best method on this task. Source code is available at http://github.com/siyueyu/SCWSSOD.

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