Recently, Hongwei Li’s team from China University of Geosciences published a research article titled “scSCC: A swapped contrastive learning-based clustering method for single-cell gene ...

By introducing a novel top-k queue-based contrastive learning strategy, the framework significantly improves the model’s accuracy in distinguishing challenging positive and negative samples and its ...

On the one hand, FedSC introduces an inter-contrastive learning strategy to bring instance-level embeddings closer to relational prototypes with the same semantics and away from distinct classes.

Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation This repo is the official project repository of the papers: " AMContrast3D " and "AMContrast3D++".

Unsupervised domain adaptation (UDA) for remote sensing image semantic segmentation aims to train a deep model on the labeled source domain and apply it to the unlabeled target domain. However, ...

Unsupervised domain adaptation for remote sensing image semantic segmentation aims to train a deep model on the labeled source domain and apply it to the unlabeled target domain. However, resolution ...

The semantic segmentation of RGB-D images involves understanding objects appearances and spatial relationships within a scene, which necessitates careful con ...

SupCAM: Chromosome cluster types identification using supervised contrastive learning with category-variant augmentation and self-margin loss ...