# Adaptive Masked Proxies for Few-Shot Segmentation Source: [Adaptive Masked Proxies for Few-Shot Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:59:47.071Z Authors: Mennatullah Siam, Boris Oreshkin, Martin Jagersand Published: 2019-02-19T22:28:02.000Z ## Abstract Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method that constructs the final segmentation layer weights from few labelled samples. It utilizes multi-resolution average pooling on base embeddings masked with the label to act as a positive proxy for the new class, while fusing it with the previously learned class signatures. Our method is evaluated on PASCAL-$5^i$ dataset and outperforms the state-of-the-art in the few-shot semantic segmentation. Unlike previous methods, our approach does not require a second branch to estimate parameters or prototypes, which enables it to be used with 2-stream motion and appearance based segmentation networks. We further propose a novel setup for evaluating continual learning of object segmentation which we name incremental PASCAL (iPASCAL) where our method outperforms the baseline method. Our code is publicly available at https://github.com/MSiam/AdaptiveMaskedProxies. ## 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 Semantic Segmentation on PASCAL-5^i (test)](): Few-shot Semantic Segmentation; PASCAL-5^i (test); FB-IoU: 62.1. ## Original sources and implementation [Original paper]() [PDF]()