Meta-learning has gained wide popularity as a training framework that is more data-efficient
than traditional machine learning methods. However, its generalization ability in complex …

Deep neural networks have demonstrated advanced abilities on various visual classification
tasks, which heavily rely on the large-scale training samples with annotated ground-truth …

In this paper, we study a novel problem in egocentric action recognition, which we term as"
Multimodal Generalization"(MMG). MMG aims to study how systems can generalize when …

Although providing exceptional results for many computer vision tasks, state-of-the-art deep
learning algorithms catastrophically struggle in low data scenarios. However, if data in …

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 …

Learning to recognize novel visual categories from a few examples is a challenging task for
machines in real-world industrial applications. In contrast, humans have the ability to …

The capability of learning and generalizing from very few samples successfully is a
noticeable demarcation separating artificial intelligence and human intelligence. Despite the …

The deep forest (DF) model is built using a multilayer ensemble of forest units through
decision tree aggregation. DF presents characteristics of an easy-to-understand structure, is …

Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of
images (eg, birds, flowers, and airplanes) belonging to different subclasses of the same …