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 …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …

Detecting objects and estimating their viewpoints in images are key tasks of 3D scene
understanding. Recent approaches have achieved excellent results on very large …

In few-shot learning scenarios, the challenge is to generalize and perform well on new
unseen examples when only very few labeled examples are available for each task. Model …

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL)
methods: the pre-trained knowledge is indeed a confounder that limits the performance. This …

Abstract We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our
method maximizes the mutual information between the query features and their label …

We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any
feature embedding learned from the base classes, we minimize a quadratic binary …

Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared
with inductive few-shot learning, transductive models typically perform better as they …

A fundamental assumption of most machine learning algorithms is that the training and test
data are drawn from the same underlying distribution. However, this assumption is violated …