Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms
that can continually learn new concepts from a few data points, without forgetting knowledge …

Deep generative models, which target reproducing the data distribution to produce novel
images, have made unprecedented advancements in recent years. However, one critical …

In this paper, we tackle the problem of novel visual category discovery, ie, grou**
unlabelled images from new classes into different semantic partitions by leveraging a …

Synthetic aperture radar (SAR) target recognition faces the challenge that there are very little
labeled data. Although few-shot learning methods are developed to extract more information …

Few-shot learning aims to train efficient predictive models with a few examples. The lack of
training data leads to poor models that perform high-variance or low-confidence predictions …

Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to
accommodate new tasks, given only a few examples. To handle the limited-data in few-shot …

Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with
only a limited amount of labeled data. Research literature on few-shot learning exhibits great …

Encouraging progress in few-shot semantic segmentation has been made by leveraging
features learned upon base classes with sufficient training data to represent novel classes …

Continual semantic segmentation (CSS) aims to extend an existing model to tackle unseen
tasks while retaining its old knowledge. Naively fine-tuning the old model on new data leads …

Learning discriminative features from very few labeled samples to identify novel classes has
received increasing attention in skeleton-based action recognition. Existing works aim to …