# Semi-Supervised Video Salient Object Detection Using Pseudo-Labels

Pengxiang Yan1   Guanbin Li1   Yuan Xie1   Zhen Li2   Chuan Wang3   Tianshui Chen1   Liang Lin1

1Sun Yat-sen University                  2CUHK (Shenzhen)                  3Megvii Technology

Accepted by ICCV 2019

Figure: The architecture of our flow guided pseudo-label generation model (FGPLG)

### Abstract

Deep learning based video salient object detection has recently achieved great success with its performance significantly outperforming any other unsupervised methods. However, existing data-driven approaches heavily rely on a large quantity of pixel-wise annotated video frames to deliver such promising results. In this paper, we address the semi-supervised video salient object detection task using pseudo-labels. Specifically, we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module. Based on the same refinement network and motion information in terms of optical flow, we further propose a novel method to generate pixellevel pseudo-labels from sparsely annotated frames. By utilizing the generated pseudo-labels together with a part of manual annotations, our video saliency detector learns spatial and temporal cues for both contrast inference and coherence enhancement, thus producing accurate saliency maps. Experimental results demonstrate that our proposed semi-supervised method even greatly outperforms all the state-of-the-art fully-supervised methods across three public benchmarks of VOS, DAVIS, and FBMS.

@article{yan2019semi,