# Boosting Few-shot Semantic Segmentation with Transformers Source: [Boosting Few-shot Semantic Segmentation with Transformers]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:26:28.956Z Authors: Guolei Sun, Yun Liu, Jingyun Liang, Luc Van Gool Published: 2021-08-04T20:09:21.000Z ## Abstract Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentation has attracted lots of research attention. Previous arts extract features from support and query images, which are processed jointly before making predictions on query images. The whole process is based on convolutional neural networks (CNN), leading to the problem that only local information is used. In this paper, we propose a TRansformer-based Few-shot Semantic segmentation method (TRFS). Specifically, our model consists of two modules: Global Enhancement Module (GEM) and Local Enhancement Module (LEM). GEM adopts transformer blocks to exploit global information, while LEM utilizes conventional convolutions to exploit local information, across query and support features. Both GEM and LEM are complementary, helping to learn better feature representations for segmenting query images. Extensive experiments on PASCAL-5i and COCO datasets show that our approach achieves new state-of-the-art performance, demonstrating its effectiveness. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Few-shot Segmentation on PASCAL-5i](): Few-shot Segmentation; PASCAL-5i; mIoU (Fold 0): 65. - [Few-Shot Semantic Segmentation on COCO-20i](): Few-shot Semantic Segmentation; COCO-20i; mIoU: 38.9. ## Original sources and implementation [Original paper]() [PDF]()