Semi-supervised Salient Object Detection with Effective Confidence Estimation

• URL: http://arxiv.org/abs/2112.14019v2
• Date: Sun, 26 Nov 2023 00:40:15 GMT
• Title: Semi-supervised Salient Object Detection with Effective Confidence Estimation
• Authors: Jiawei Liu, Jing Zhang, Nick Barnes
• Abstract summary: We study semi-supervised salient object detection with access to a small number of labeled samples and a large number of unlabeled samples. We model the nature of human saliency labels using the latent variable of the Conditional Energy-based Model. With only 1/16 labeled samples, our model achieves competitive performance compared with state-of-the-art fully-supervised models.
• Score: 35.0990691497574
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The success of existing salient object detection models relies on a large pixel-wise labeled training dataset, which is time-consuming and expensive to obtain. We study semi-supervised salient object detection, with access to a small number of labeled samples and a large number of unlabeled samples. Specifically, we present a pseudo label based learn-ing framework with a Conditional Energy-based Model. We model the stochastic nature of human saliency labels using the stochastic latent variable of the Conditional Energy-based Model. It further enables generation of a high-quality pixel-wise uncertainty map, highlighting the reliability of corresponding pseudo label generated for the unlabeled sample. This minimises the contribution of low-certainty pseudo labels in optimising the model, preventing the error propagation. Experimental results show that the proposed strategy can effectively explore the contribution of unlabeled data. With only 1/16 labeled samples, our model achieves competitive performance compared with state-of-the-art fully-supervised models.

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