80 citations · 238 across the 19 of their papers we have counts for
19 papers
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering
Xinyue Hu, Lin Gu, Qiyuan An +6
To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair of main and reference ima…
Sketch-based Medical Image Retrieval
Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya +12
The amount of medical images stored in hospitals is increasing faster than ever; however, utilizing the accumulated medical images has been limited. This is because existing conten…
Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fields
Ziteng Cui, Lin Gu, Xiao Sun +3
Common capture low-light scenes are challenging for most computer vision techniques, including Neural Radiance Fields (NeRF). Vanilla NeRF is viewer-centred simplifies the renderin…
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
Xinyue Hu, Lin Gu, Kazuma Kobayashi +6
Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of m…
Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer
Shenghan Su, Lin Gu, Yue Yang +2
The long-standing theory that a colour-naming system evolves under dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studie…
Improving Fairness in Image Classification via Sketching
Ruichen Yao, Ziteng Cui, Xiaoxiao Li +1
Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system. However, deep neural networks (DNNs) tend to make unfair predictions w…