# Meta-DRN: Meta-Learning for 1-Shot Image Segmentation Source: [Meta-DRN: Meta-Learning for 1-Shot Image Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:29:00.055Z Authors: Atmadeep Banerjee Published: 2020-08-01T11:23:37.000Z ## Abstract Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation. The proposed model is created by taking inspiration from well-performing architectures for semantic segmentation and adapting it to the 1-shot domain. We train our model using 4 meta-learning algorithms that have worked well for image classification and compare the results. For the chosen dataset, our proposed model has a 70% lower parameter count than the benchmark, while having better or comparable mean IoU scores using all 4 of the meta-learning algorithms. ## 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]()