Texture-guided Saliency Distilling for Unsupervised Salient Object Detection

• URL: http://arxiv.org/abs/2207.05921v3
• Date: Tue, 9 May 2023 04:21:48 GMT
• Title: Texture-guided Saliency Distilling for Unsupervised Salient Object Detection
• Authors: Huajun Zhou and Bo Qiao and Lingxiao Yang and Jianhuang Lai and Xiaohua Xie
• Abstract summary: We propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. Our method achieves state-of-the-art USOD performance on RGB, RGB-D, RGB-T, and video SOD benchmarks.
• Score: 67.10779270290305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundary. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance.

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