Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared
with inductive few-shot learning, transductive models typically perform better as they …

Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …

In instance image retrieval, considering local spatial information within an image has proven
effective to boost retrieval performance, as demonstrated by local visual descriptor based …

Traditional approaches for learning on categorical data underexploit the dependencies
between columns (aka fields) in a dataset because they rely on the embedding of data …

Adversarially robust knowledge distillation aims to compress large-scale models into
lightweight models while preserving adversarial robustness and natural performance on a …

Video sequences exhibit significant nuisance variations (undesired effects) of speed of
actions, temporal locations, and subjects' poses, leading to temporal-viewpoint …

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline
in cognitive functions and eventually results in death. An early AD diagnosis is important for …

Adversarial robustness generally relies on large-scale architectures and datasets, hindering
resource-efficient deployment. For scalable solutions, adversarially robust knowledge …

Recent advances achieved by deep learning models rely on the independent and identically
distributed assumption, hindering their applications in real-world scenarios with domain …

Human action recognition is currently one of the most active research areas in computer
vision. Various research studies indicate that the performance of action recognition is highly …