277 citations · 1.7k across the 70 of their papers we have counts for
73 papers
Large-Market Discipline in Combinatorial Double Auctions: No Assembly, Bundle Selection, and Complementarities
Konstantinos E. Zachariadis, Yongxin Yang
We study double auctions for markets in which goods are valuable in bundles, such as data, model weights, and fine-tuned AI assets. A key friction in such markets is No Assembly: a…
Spectral Attention Steering for Prompt Highlighting
Weixian Waylon Li, Yuchen Niu, Yongxin Yang +3
Attention steering is an important technique for controlling model focus, enabling capabilities such as prompt highlighting, where the model prioritises user-specified text. Howeve…
MedVision: Benchmarking Quantitative Medical Image Analysis
Yongcheng Yao, Yongshuo Zong, Raman Dutt +3
Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks.…
Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration
Junyuan Deng, Xinyi Wu, Yongxing Yang +3
Recently, pre-trained text-to-image (T2I) models have been extensively adopted for real-world image restoration because of their powerful generative prior. However, controlling the…
Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities
Raman Dutt, Harleen Hanspal, Guoxuan Xia +5
In this work, we undertake the challenge of augmenting the existing generative capabilities of pre-trained text-only large language models (LLMs) with multi-modal generation capabi…
Generating Compositional Scenes via Text-to-image RGBA Instance Generation
Alessandro Fontanella, Petru-Daniel Tudosiu, Yongxin Yang +2
Text-to-image diffusion generative models can generate high quality images at the cost of tedious prompt engineering. Controllability can be improved by introducing layout conditio…
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation
Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang +5
Text-to-image generation has achieved astonishing results, yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed…
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu +2
Current vision large language models (VLLMs) exhibit remarkable capabilities yet are prone to generate harmful content and are vulnerable to even the simplest jailbreaking attacks.…
SplatMesh: Interactive 3D Segmentation and Editing Using Mesh-Based Gaussian Splatting
Kaichen Zhou, Lanqing Hong, Xinhai Chang +7
A key challenge in fine-grained 3D-based interactive editing is the absence of an efficient representation that balances diverse modifications with high-quality view synthesis unde…
Optimisation-Based Multi-Modal Semantic Image Editing
Bowen Li, Yongxin Yang, Steven McDonagh +3
Image editing affords increased control over the aesthetics and content of generated images. Pre-existing works focus predominantly on text-based instructions to achieve desired im…
OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning
Jiazheng Li, Runcong Zhao, Yongxin Yang +2
The remarkable performance of pre-trained large language models has revolutionised various natural language processing applications. Due to huge parametersizes and extensive runnin…
ChiroDiff: Modelling chirographic data with Diffusion Models
Ayan Das, Yongxin Yang, Timothy Hospedales +2
Generative modelling over continuous-time geometric constructs, a.k.a such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Suc…
Learning to Name Classes for Vision and Language Models
Sarah Parisot, Yongxin Yang, Steven McDonagh
Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges tha…
Label-Efficient Object Detection via Region Proposal Network Pre-Training
Nanqing Dong, Linus Ericsson, Yongxin Yang +2
Self-supervised pre-training, based on the pretext task of instance discrimination, has fueled the recent advance in label-efficient object detection. However, existing studies foc…
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Qishi Dong, Awais Muhammad, Fengwei Zhou +5
Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalizatio…
MEDFAIR: Benchmarking Fairness for Medical Imaging
Yongshuo Zong, Yongxin Yang, Timothy Hospedales
A multitude of work has shown that machine learning-based medical diagnosis systems can be biased against certain subgroups of people. This has motivated a growing number of bias m…
Fine-Grained VR Sketching: Dataset and Insights
Ling Luo, Yulia Gryaditskaya, Yongxin Yang +2
We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the ske…
Towards 3D VR-Sketch to 3D Shape Retrieval
Ling Luo, Yulia Gryaditskaya, Yongxin Yang +2
Growing free online 3D shapes collections dictated research on 3D retrieval. Active debate has however been had on (i) what the best input modality is to trigger retrieval, and (ii…
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection
Xiongjie Chen, Yunpeng Li, Yongxin Yang
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-worl…
Long-tail Recognition via Compositional Knowledge Transfer
Sarah Parisot, Pedro M. Esperanca, Steven McDonagh +3
In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer. Our objective is to tr…
Domain Attention Consistency for Multi-Source Domain Adaptation
Zhongying Deng, Kaiyang Zhou, Yongxin Yang +1
Most existing multi-source domain adaptation (MSDA) methods minimize the distance between multiple source-target domain pairs via feature distribution alignment, an approach borrow…
MixStyle Neural Networks for Domain Generalization and Adaptation
Kaiyang Zhou, Yongxin Yang, Yu Qiao +1
Neural networks do not generalize well to unseen data with domain shifts -- a longstanding problem in machine learning and AI. To overcome the problem, we propose MixStyle, a simpl…
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many h…
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images
Nanqing Dong, Matteo Maggioni, Yongxin Yang +3
This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals,…