Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

Recognizing human activities from video sequences or still images is a challenging task due
to problems, such as background clutter, partial occlusion, changes in scale, viewpoint …

Abstract We propose Prototypical Networks for the problem of few-shot classification, where
a classifier must generalize to new classes not seen in the training set, given only a small …

Due to the importance of zero-shot learning, ie, classifying images where there is a lack of
labeled training data, the number of proposed approaches has recently increased steadily …

Abstract Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D
zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP …

Learning with limited data is a key challenge for visual recognition. Many few-shot learning
methods address this challenge by learning an instance embedding function from seen …

Existing zero-shot learning (ZSL) models typically learn a projection function from a feature
space to a semantic embedding space (eg attribute space). However, such a projection …

Zero-shot learning consists in learning how to recognize new concepts by just having a
description of them. Many sophisticated approaches have been proposed to address the …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

Due to the importance of zero-shot learning, the number of proposed approaches has
increased steadily recently. We argue that it is time to take a step back and to analyze the …