Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions

• URL: http://arxiv.org/abs/2012.04829v1
• Date: Wed, 9 Dec 2020 02:36:52 GMT
• Title: Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions
• Authors: Jun Wang, Shaoguo Wen, Kaixing Chen, Jianghua Yu, Xin Zhou, Peng Gao, Changsheng Li, Guotong Xie
• Abstract summary: We propose a novel and principled semi-supervised active learning framework for instance segmentation. Specifically, we present an uncertainty sampling strategy named Triplet Scoring Predictions (TSP) to explicitly incorporate samples ranking clues from classes, bounding boxes and masks. Results on medical images datasets demonstrate that the proposed method results in the embodiment of knowledge from available data in a meaningful way.
• Score: 25.408505612498423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object detection. However, its potential has not been explored in the more complex instance segmentation task that usually has relatively higher annotation cost. In this paper, we propose a novel and principled semi-supervised active learning framework for instance segmentation. Specifically, we present an uncertainty sampling strategy named Triplet Scoring Predictions (TSP) to explicitly incorporate samples ranking clues from classes, bounding boxes and masks. Moreover, we devise a progressive pseudo labeling regime using the above TSP in semi-supervised manner, it can leverage both the labeled and unlabeled data to minimize labeling effort while maximize performance of instance segmentation. Results on medical images datasets demonstrate that the proposed method results in the embodiment of knowledge from available data in a meaningful way. The extensive quantitatively and qualitatively experiments show that, our method can yield the best-performing model with notable less annotation costs, compared with state-of-the-arts.

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