Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Contrastive learning-based video-language representation learning approaches, eg, CLIP,
have achieved outstanding performance, which pursue semantic interaction upon pre …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

We study graph data augmentation by mixup, which has been used successfully on images.
A key operation of mixup is to compute a convex combination of a pair of inputs. This …

Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with
natural language descriptions. Current methods either fail to leverage the local details or are …

Supervised learning of skeleton sequence encoders for action recognition has received
significant attention in recent times. However, learning such encoders without labels …

With rapid advancements in depth sensors and deep learning, skeleton-based person re-
identification (re-ID) models have recently achieved remarkable progress with many …

Mixup refers to interpolation-based data augmentation, originally motivated as a way to go
beyond empirical risk minimization (ERM). Its extensions mostly focus on the definition of …

Biological networks are commonly used in biomedical and healthcare domains to effectively
model the structure of complex biological systems with interactions linking biological entities …

Graph matching is a commonly used technique in computer vision and pattern recognition.
Recent data-driven approaches have improved the graph matching accuracy remarkably …