Noise-Sensitive Adversarial Learning for Weakly Supervised Salient Object Detection

Abstract

Weakly supervised salient object detection (WSOD) aims at training saliency detection models with weak supervision. Normally, the WSOD methods use pseudo labels converted from image-level classification labels to train the saliency network. However, the converted pseudo labels always contain noise information compared to ground truth. Previous methods are directly affected by pseudo label noise to generate error-prone predictions. To mitigate this problem, we design a noise-robust adversarial learning framework and propose a noise-sensitive training strategy for the framework. The framework consists of a saliency network and a noise-robust discriminator network. With the guidance of noise-robust discriminator network, our saliency network is robust to noise information in pseudo labels. The proposed noise-sensitive training strategy can make good use of both superior and inferior samples in the pseudo label dataset. With the noise-sensitive training strategy, our framework can further balance the learning of saliency information and the robustness of noise information. Comprehensive experiments on five public datasets demonstrate that our method outperforms the existing image-level classification label based WSOD methods.