Displaying 25 resources
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Word-Class Embeddings for Multiclass Text Classification

Code for Word-Class Embeddings (WCEs), a form of supervised embeddings especially suited for multiclass text classification.

Category
Semantic knowledge, Technology methodologies and landscape
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

CO2A – Contrastive Conditional domain Alignment

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

Category
Multi-modal interaction, Sensing of motion and mechanical properties
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

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.

Category
Semantic knowledge
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

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.

Category
Semantic knowledge
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

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.

Category
Semantic knowledge
Target audience
ADR Experts and Associations, Researchers and Academic
Source
Adra-e
Software resources Software resources

pygrank

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

Category
Multi-modal interaction
Target audience
ADR Experts and Associations, Private Sector, Researchers and Academic
Source
Adra-e