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 …

In this paper, we address the few-shot classification task from a new perspective of optimal
matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to …

Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and
unseen classes, when only the labeled examples from seen classes are provided. Recent …

Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts
dedicated to overcoming the problems of visual-semantic domain gaps and seen-unseen …

Recent developments in multimodal training methodologies, including CLIP and ALIGN,
obviate the necessity for individual data labeling. These approaches utilize pairs of data and …

The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge
between visual and attribute features on seen classes, and thus achieving a desirable …

We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-
Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by …