A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation
This addresses the challenge of reducing annotation costs for semantic segmentation in few-shot learning scenarios, though it is incremental as it builds on existing meta-learning frameworks.
The paper tackles the problem of weakly supervised few-shot semantic segmentation, where only image-level labels are available, by proposing a pixel-level meta-learner that predicts pseudo masks and uses them for segmentation. It achieves satisfactory results in fully supervised settings and outperforms state-of-the-art methods in weakly supervised settings.
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.