Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. 2) Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. 3) Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained 97%-99% performance of its fully-supervised version with only 10 annotated points per image.

Authors

• Zhenyu Wu Xi'an Jiaotong University
• Lin Wang
• Wei Wang
• Qing Xia SenseTime Group Limited
• Chenglizhao Chen Qingdao Institute of Software, China University of Petroleum (East China) College of Computer Science and Technology, China University of Petroleum (East China) Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software
• Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems, Beihang University
• Shuo Li Case Western Reserve University

Keywords

• CV: Low Level & Physics-based Vision
• CV: Segmentation