Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation

• URL: http://arxiv.org/abs/2202.06498v1
• Date: Mon, 14 Feb 2022 06:16:26 GMT
• Title: Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation
• Authors: Jun Seo, Young-Hyun Park, Sung Whan Yoon, Jaekyun Moon
• Abstract summary: Few-shot learning allows machines to classify novel classes using only a few labeled samples. We propose a learnable module that can be placed on top of existing segmentation networks for performing few-shot segmentation. Experiments on PASCAL-$5i$ and COCO-$20i$ datasets confirm that the added modules successfully extend the capability of existing segmentators.
• Score: 21.276981570672064
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable module that can be placed on top of existing segmentation networks for performing few-shot segmentation. This module, called the task-adaptive feature transformer (TAFT), linearly transforms task-specific high-level features to a set of task agnostic features well-suited to conducting few-shot segmentation. The task-conditioned feature transformation allows an effective utilization of the semantic information in novel classes to generate tight segmentation masks. We also propose a semantic enrichment (SE) module that utilizes a pixel-wise attention module for high-level feature and an auxiliary loss from an auxiliary segmentation network conducting the semantic segmentation for all training classes. Experiments on PASCAL-$5^i$ and COCO-$20^i$ datasets confirm that the added modules successfully extend the capability of existing segmentators to yield highly competitive few-shot segmentation performances.

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