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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.
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
Conventional detection networks usually need abundant labeled training samples, while humans can learn new concepts incrementally with just a few examples. This paper focuses on a more challenging but realistic class-incremental few-shot object detec
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task requires to understand diverse levels of visual cues and analyze fine-g
Few-shot segmentation targets to segment new classes with few annotated images provided. It is more challenging than traditional semantic segmentation tasks that segment known classes with abundant annotated images. In this paper, we propose a Protot
Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. It is a challenging task since the dense prediction can only be achieved under the guidance of latent features defined by sparse annotations. Exi