
Neighborhood Contrastive Learning for Novel Class Discovery
A holistic learning framework for Novel Class Discovery (NCD), which adopts contrastive learning to learn discriminate features with both the labeled and unlabeled data.

Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
we study the task of synthetic-to-real domain generalized semantic segmentation, which aims to learn a model that is robust to unseen real-world scenes using only synthetic data.

Augmentation-free unsupervised approach for point clouds
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.

Novel Class Discovery in Semantic Segmentation (NCDSS)
We introduce a new setting of Novel Class Discovery in Semantic Segmentation (NCDSS), which aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes.

Open e-Learning Platform on Artificial Intelligence for Primary Education
This e-learning space contains an ensemble of multilingual AI-STEM teaching and learning materials to support teachers and school students acquire digital skills and key competencies.

Machine Learning for Kids
This free tool introduces machine learning by providing hands-on experiences for training machine learning systems and building things with them.