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Register a new userA Few Guidelines for Incremental Few-Shot Segmentation
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Fabio Cermelli
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Reducing the amount of supervision required by neural networks is especially important in the context of semantic segmentation, where collecting dense pixel-level annotations is particularly expensive. In this paper, we address this problem from a new perspective: Incremental Few-Shot Segmentation. In particular, given a pretrained segmentation model and few images containing novel classes, our goal is to learn to segment novel classes while retaining the ability to segment previously seen ones. In this context, we discover, against all beliefs, that fine-tuning the whole architecture with these few images is not only meaningful, but also very effective. We show how the main problems of end-to-end training in this scenario are i) the drift of the batch-normalization statistics toward novel classes that we can fix with batch renormalization and ii) the forgetting of old classes, that we can fix with regularization strategies. We summarize our findings with five guidelines that together consistently lead to the state of the art on the COCO and Pascal-VOC 2012 datasets, with different number of images per class and even with multiple learning episodes.
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The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old classes by stabilizing NGs topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental learning methods on CIFAR100, miniImageNet, and CUB200 datasets.
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