A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation

• URL: http://arxiv.org/abs/2111.01418v1
• Date: Tue, 2 Nov 2021 08:28:11 GMT
• Title: A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation
• Authors: Yuan-Hao Lee, Fu-En Yang, Yu-Chiang Frank Wang
• Abstract summary: Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. We propose a novel meta-learning framework, which predicts pseudo pixel-level segmentation masks from a limited amount of data and their semantic labels. Our proposed learning model can be viewed as a pixel-level meta-learner.
• Score: 40.27705176115985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. One is typically required to collect a large mount of data (i.e., base classes) with such ground truth information, followed by meta-learning strategies to address the above learning task. When only image-level semantic labels can be observed during both training and testing, it is considered as an even more challenging task of weakly supervised few-shot semantic segmentation. To address this problem, we propose a novel meta-learning framework, which predicts pseudo pixel-level segmentation masks from a limited amount of data and their semantic labels. More importantly, our learning scheme further exploits the produced pixel-level information for query image inputs with segmentation guarantees. Thus, our proposed learning model can be viewed as a pixel-level meta-learner. Through extensive experiments on benchmark datasets, we show that our model achieves satisfactory performances under fully supervised settings, yet performs favorably against state-of-the-art methods under weakly supervised settings.

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