Learned language-conditioned robot policies often struggle to effectively adapt to new real-
world tasks even when pre-trained across a diverse set of instructions. We propose a novel …

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 …

Meta-learning is one of the important methods to solve the challenging few-shot learning
setting by using previous knowledge and experience to guide the learning of new tasks …

Few-shot models aim at making predictions using a minimal number of labeled examples
from a given task. The main challenge in this area is the one-shot setting where only one …

Surface defect classification plays a very important role in industrial production and
mechanical manufacturing. However, there are currently some challenges hindering its use …

The efficacy of surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) in
addressing expensive multi-objective optimization problems (MOPs) is contingent upon the …

Few-Shot learning aims to train models which can adapt to previously unseen tasks based
on small amounts of data. One of the leading Few-Shot learning approaches is Model …

Few-shot classification (FSC) is a challenging task due to limitation in accessing training
data. Recent methods often employ highly complex networks to obtain high-quality features …

Existing few-shot learning (FSL) methods usually treat each sample as a single feature point
or utilize intra-class feature transformation to augment features. However, few-shot novel …

This study presents a novel approach for real-time vision-based structural health monitoring,
focusing on evaluating the deformation state of lattice structures. The structures are …