# SML: Semantic Meta-learning for Few-shot Semantic Segmentation Source: [SML: Semantic Meta-learning for Few-shot Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:44.557Z Authors: Ayyappa Kumar Pambala, Titir Dutta, Soma Biswas Published: 2020-09-14T18:26:46.000Z ## Abstract The significant amount of training data required for training Convolutional Neural Networks has become a bottleneck for applications like semantic segmentation. Few-shot semantic segmentation algorithms address this problem, with an aim to achieve good performance in the low-data regime, with few annotated training images. Recently, approaches based on class-prototypes computed from available training data have achieved immense success for this task. In this work, we propose a novel meta-learning framework, Semantic Meta-Learning (SML) which incorporates class level semantic descriptions in the generated prototypes for this problem. In addition, we propose to use the well established technique, ridge regression, to not only bring in the class-level semantic information, but also to effectively utilise the information available from multiple images present in the training data for prototype computation. This has a simple closed-form solution, and thus can be implemented easily and efficiently. Extensive experiments on the benchmark PASCAL-5i dataset under different experimental settings show the effectiveness of the proposed framework. ## 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. ## Original sources and implementation [Original paper]() [PDF]()