Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels

Yongri Piao, Chenyang Lu, Miao Zhang, Huchuan Lu

Keywords: Semi-Supervised Video Salient Object Detection, Adversarial Learning

TL;DR: We propose an Uncertainty-Guided Pseudo Label Generator and introduce an adversarial learning strategy to improve the quality of pseudo-labels, finally solving SS-VSOD by using the progressively-enhanced pseudo labels.

Abstract: Semi-Supervised Video Salient Object Detection (SS-VSOD) is challenging because of the lack of temporal information in video sequences caused by sparse annotations. Most works address this problem by generating pseudo labels for unlabeled data. However, error-prone pseudo labels negatively affect the VOSD model. Therefore, a deeper insight into pseudo labels should be developed. In this work, we aim to explore 1) how to utilize the incorrect predictions in pseudo labels to guide the network to generate more robust pseudo labels and 2) how to further screen out the noise that still exists in the improved pseudo labels. To this end, we propose an Uncertainty-Guided Pseudo Label Generator (UGPLG), which makes full use of inter-frame information to ensure the temporal consistency of the pseudo labels and improves the robustness of the pseudo labels by strengthening the learning of difficult scenarios. Furthermore, we also introduce the adversarial learning to address the noise problems in pseudo labels, guaranteeing the positive guidance of pseudo labels during model training. Experimental results demonstrate that our methods outperform existing semi-supervised method and partial fully-supervised methods across five public benchmarks of DAVIS, FBMS, MCL, ViSal and SegTrack-V2.