Few-Shot Segmentation via Cycle-Consistent Transformer

TL;DR

This paper introduces CyCTR, a novel cycle-consistent transformer that leverages pixel-wise support-query relationships for improved few-shot segmentation, significantly outperforming previous methods on standard benchmarks.

Contribution

The paper proposes a cycle-consistent attention mechanism within a transformer framework to better utilize pixel-wise support information for few-shot segmentation.

Findings

Achieves 67.5% mIoU on Pascal-5^i for 5-shot segmentation.

Achieves 45.6% mIoU on COCO-20^i for 5-shot segmentation.

Outperforms previous state-of-the-art methods by 5.6% and 7.1%.

Abstract

Few-shot segmentation aims to train a segmentation model that can fast adapt to novel classes with few exemplars. The conventional training paradigm is to learn to make predictions on query images conditioned on the features from support images. Previous methods only utilized the semantic-level prototypes of support images as conditional information. These methods cannot utilize all pixel-wise support information for the query predictions, which is however critical for the segmentation task. In this paper, we focus on utilizing pixel-wise relationships between support and query images to facilitate the few-shot segmentation task. We design a novel Cycle-Consistent TRansformer (CyCTR) module to aggregate pixel-wise support features into query ones. CyCTR performs cross-attention between features from different images, i.e. support and query images. We observe that there may exist unexpected irrelevant pixel-level support features. Directly performing cross-attention may aggregate these features from support to query and bias the query features. Thus, we propose using a novel cycle-consistent attention mechanism to filter out possible harmful support features and encourage query features to attend to the most informative pixels from support images. Experiments on all few-shot segmentation benchmarks demonstrate that our proposed CyCTR leads to remarkable improvement compared to previous state-of-the-art methods. Specifically, on Pascal-$5^i$ and COCO-$20^i$ datasets, we achieve 67.5% and 45.6% mIoU for 5-shot segmentation, outperforming previous state-of-the-art methods by 5.6% and 7.1% respectively.