Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation

To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm. However, most existing methods only focus on the traditional 1-way segmentation setting (i. e. , one image only contains a single object). This is far away from practical semantic segmentation tasks where the K-way setting (K >1) is usually required by performing the accurate multi-object segmentation. To deal with this issue, we formulate the fewshot semantic segmentation task as a learning-based pixel classification problem, and propose a novel framework called MetaSegNet based on meta-learning. In MetaSegNet, an architecture of embedding module consisting of the global and local feature branches is developed to extract the appropriate meta-knowledge for the few-shot segmentation. Moreover, we incorporate a linear model into MetaSegNet as a base learner to directly predict the label of each pixel for the multiobject segmentation. Furthermore, our MetaSegNet can be trained by the episodic training mechanism in an end-to-end manner from scratch. Experiments on two popular semantic segmentation datasets, i. e. , PASCAL VOC and COCO, reveal the effectiveness of the proposed MetaSegNet in the K-way few-shot semantic segmentation task.

Authors

• Pinzhuo Tian Shanghai University
• Zhangkai Wu
• Lei Qi School of Computer Science and Engineering, Southeast University, China
• Lei Wang
• Yinghuan Shi National Key Laboratory for Novel Software Technology, Nanjing University, China
• Yang Gao Nanjing University

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