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 …

Abstract We design a Kernelized Few-shot Object Detector by leveraging kernelized
matrices computed over multiple proposal regions, which yield expressive non-linear …

Neural networks notoriously suffer from the problem of catastrophic forgetting, the
phenomenon of forgetting the past knowledge when acquiring new knowledge. Overcoming …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Many few-shot learning models focus on recognising images. In contrast, we tackle a
challenging task of few-shot action recognition from videos. We build on a C3D encoder for …

Few-shot image generation (FSIG) aims to learn to generate new and diverse samples given
an extremely limited number of samples from a domain, eg, 10 training samples. Recent …

Abstract We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich
and robust feature representation in the context of few-shot image classification. Previous …

Abstract Model-agnostic meta-learning (MAML) is one of the most successful meta-learning
algorithms. It has a bi-level optimization structure where the outer-loop process learns a …

We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint
Temporal and Camera Viewpoint Alignment. To factor out misalignment between query and …

Human actions in video sequences are characterized by the complex interplay between
spatial features and their temporal dynamics. In this paper, we propose novel tensor …