Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

We show that the way inference is performed in few-shot segmentation tasks has a
substantial effect on performances--an aspect often overlooked in the literature in favor of …

Episodic learning is a popular practice among researchers and practitioners interested in
few-shot learning. It consists of organising training in a series of learning problems (or …

Graph representation learning has attracted tremendous attention due to its remarkable
performance in many real-world applications. However, prevailing supervised graph …

Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many
existing works take the meta-learning approach, constructing a few-shot learner (a meta …

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by
leveraging experience from\emph {related} training tasks. In this paper, we try to understand …

This paper focuses on the data-insufficiency problem in multi-task learning within an
episodic training setup. Specifically, we explore the potential of heterogeneous information …

Large language models (LLMs) have achieved remarkable success across a wide spectrum
of tasks; however, they still face limitations in scenarios that demand long-term planning and …

Semantic segmentation assigns category labels to each pixel in an image, enabling
breakthroughs in fields such as autonomous driving and robotics. Deep Neural Networks …