DRTN: Dual Relation Transformer Network with Feature Erasure and Contrastive Learning for Multi-Label Image Classification

Information Science, Computer Science, Artificial Intelligence,
Multi-label image classification   Transformer   Pseudo-region   Feature erasure   Contrastive learning  

New Breakthrough in Multi-Label Image Classification: Dual Relation Transformer Network Academic Background Multi-Label Image Classification (MLIC) is a fundamental yet highly challenging problem in the field of computer vision. Unlike single-label image classification, MLIC aims to assign multiple labels to objects within a single image. Due to th...

Exploiting Instance-Label Dynamics through Reciprocal Anchored Contrastive Learning for Few-Shot Relation Extraction

Information Science, Computer Science, Artificial Intelligence,
Few-shot relation extraction   Contrastive learning   Pre-train   Fine-tune   Information extraction  

Exploiting Instance-Label Dynamics through Reciprocal Anchored Contrastive Learning for Few-Shot Relation Extraction Academic Background In the field of Natural Language Processing (NLP), Relation Extraction (RE) is a fundamental task aimed at identifying and extracting relationships between entities in text. However, traditional supervised learnin...