6 citations · 18 across the 12 of their papers we have counts for
12 papers
ADaFuSE: Adaptive Diffusion-generated Image and Text Fusion for Interactive Text-to-Image Retrieval
Zhuocheng Zhang, Xingwu Zhang, Kangheng Liang +3
Recent advances in interactive text-to-image retrieval (I-TIR) use diffusion models to bridge the modality gap between the textual information need and the images to be searched, r…
Diffusion Augmented Retrieval: A Training-Free Approach to Interactive Text-to-Image Retrieval
Zijun Long, Kangheng Liang, Gerardo Aragon-Camarasa +2
Interactive Text-to-image retrieval (I-TIR) is an important enabler for a wide range of state-of-the-art services in domains such as e-commerce and education. However, current meth…
CFIR: Fast and Effective Long-Text To Image Retrieval for Large Corpora
Zijun Long, Xuri Ge, Richard Mccreadie +1
Text-to-image retrieval aims to find the relevant images based on a text query, which is important in various use-cases, such as digital libraries, e-commerce, and multimedia datab…
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning
Zijun Long, Lipeng Zhuang, George Killick +3
Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-rese…
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion
Zijun Long, George Killick, Lipeng Zhuang +3
State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning…
CrisisViT: A Robust Vision Transformer for Crisis Image Classification
Zijun Long, Richard McCreadie, Muhammad Imran
In times of emergency, crisis response agencies need to quickly and accurately assess the situation on the ground in order to deploy relevant services and resources. However, autho…
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-…