7 citations · 29 across the 17 of their papers we have counts for
7 papers · 1 filter
Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning
Zijun Long, George Killick, Lipeng Zhuang +3
Image classification datasets exhibit a non-negligible fraction of mislabeled examples, often due to human error when one class superficially resembles another. This issue poses ch…
Large Multi-modal Encoders for Recommendation
Zixuan Yi, Zijun Long, Iadh Ounis +2
In recent years, the rapid growth of online multimedia services, such as e-commerce platforms, has necessitated the development of personalised recommendation approaches that can e…
RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models
Zijun Long, George Killick, Richard McCreadie +1
Robotic vision applications often necessitate a wide range of visual perception tasks, such as object detection, segmentation, and identification. While there have been substantial…
MultiWay-Adapater: Adapting large-scale multi-modal models for scalable image-text retrieval
Zijun Long, George Killick, Richard McCreadie +1
As Multimodal Large Language Models (MLLMs) grow in size, adapting them to specialized tasks becomes increasingly challenging due to high computational and memory demands. Indeed,…
When hard negative sampling meets supervised contrastive learning
Zijun Long, George Killick, Richard McCreadie +2
State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss. Many studies have shown that using…
LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision Transformers
Zijun Long, Zaiqiao Meng, Gerardo Aragon Camarasa +1
Vision Transformers (ViTs) have emerged as popular models in computer vision, demonstrating state-of-the-art performance across various tasks. This success typically follows a two-…
Divergence-Based Domain Transferability for Zero-Shot Classification
Alexander Pugantsov, Richard McCreadie
Transferring learned patterns from pretrained neural language models has been shown to significantly improve effectiveness across a variety of language-based tasks, meanwhile furth…