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

• URL: http://arxiv.org/abs/2212.06493v1
• Date: Tue, 13 Dec 2022 11:18:08 GMT
• Title: Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
• Authors: Zhenyu Wu, Lin Wang, Wei Wang, Qing Xia, Chenglizhao Chen, Aimin Hao, Shuo Li
• Abstract summary: It is unclear whether a saliency model trained with weakly-supervised data can achieve the equivalent performance of its fully-supervised version. We propose a novel yet effective adversarial trajectory-ensemble active learning (ATAL) 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 ten annotated points per image.
• Score: 40.97103355628434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 ten annotated points per image.

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