Few-shot learning (FSL) methods typically assume clean support sets with accurately
labeled samples when training on novel classes. This assumption can often be unrealistic …

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic
model for capturing task-specific knowledge of the test task, rely only on few-labeled support …

Robust few-shot learning (RFSL), which aims to address noisy labels in few-shot learning,
has recently gained considerable attention. Existing RFSL methods are based on the …

Few-shot learning (FSL) is essential in practical applications. However, the limited training
examples make the models more vulnerable to label noise, which can lead to poor …

Meta-learning methods typically learn tasks under the assumption that all tasks are equally
important. However this assumption is often not valid. In real-world applications tasks can …

Few-shot learning has garnered significant attention in deep learning as an effective
approach for addressing the issue of data scarcity. Conventionally, training datasets in few …

Few-shot learning which aims to generalize knowledge learned from annotated base
training data to recognize unseen novel classes has attracted considerable attention …

Mainstream few-shot segmentation methods meet performance bottleneck due to the data
scarcity of novel classes with insufficient intra-class variations, which results in a biased …

Abstract The Internet of Medical Things (IoMT), with advances in wireless technologies, has
boosted traditional healthcare into smart healthcare. Computer-aided diagnosis technology …

Human intelligence is characterized by our ability to absorb and apply knowledge from the
world around us, especially in rapidly acquiring new concepts from minimal examples …