
CO2A – Contrastive Conditional domain Alignment
A novel unsupervised domain adaptation approach for action recognition from videos, inspired by recent literature on contrastive learning.

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.

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.

pygrank
pygrank is an open source framework to define, run and evaluate node ranking algorithms.

InDistill
InDistill enchances the effectiveness of the Knowledge Distillation procedure by leveraging the properties of channel pruning to both reduce the capacity gap between the models and retain the information geometry.