Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
Keywords:CV: Segmentation, CV: Low Level & Physics-Based Vision
AbstractAlthough 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.
How to Cite
Wu, Z., Wang, L., Wang, W., Xia, Q., Chen, C., Hao, A., & Li, S. (2023). Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 37(3), 2883-2891. https://doi.org/10.1609/aaai.v37i3.25390
AAAI Technical Track on Computer Vision III