Meta-DRN: Meta-Learning for 1-Shot Image Segmentation

• URL: http://arxiv.org/abs/2008.00247v1
• Date: Sat, 1 Aug 2020 11:23:37 GMT
• Title: Meta-DRN: Meta-Learning for 1-Shot Image Segmentation
• Authors: Atmadeep Banerjee
• Abstract summary: We propose a novel lightweight CNN architecture for 1-shot image segmentation. We train our model using 4 meta-learning algorithms that have worked well for image classification and compare the results.
• Score: 0.12691047660244334
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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