Texture-guided Saliency Distilling for Unsupervised Salient Object Detection
This work addresses noisy label issues in unsupervised salient object detection, which is important for computer vision applications, but it appears incremental as it builds on existing strategies.
The paper tackles the problem of noisy saliency pseudo labels in unsupervised salient object detection by proposing a method to mine saliency knowledge from both easy and hard samples, achieving state-of-the-art performance across multiple benchmarks.
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.