3 citations · 7 across the 10 of their papers we have counts for
10 papers
CoRefine: Confidence-Guided Self-Refinement for Adaptive Test-Time Compute
Chen Jin, Ryutaro Tanno, Tom Diethe +1
Large Language Models (LLMs) often rely on test-time scaling via parallel decoding (for example, 512 samples) to boost reasoning accuracy, but this incurs substantial compute. We i…
Protein generation with embedding learning for motif diversification
Kevin Michalewicz, Chen Jin, Philip Alexander Teare +4
A fundamental challenge in protein design is the trade-off between generating structural diversity while preserving motif biological function. Current state-of-the-art methods, suc…
Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
Lei Tong, Zhihua Liu, Chaochao Lu +5
We present Causal-Adapter, a modular framework that adapts frozen text-to-image diffusion backbones for counterfactual image generation. Our method supports causal interventions on…
Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation
Zhihua Liu, Amrutha Saseendran, Lei Tong +8
Open-set image segmentation poses a significant challenge because existing methods often demand extensive training or fine-tuning and generally struggle to segment unified objects…
Diffusion Instruction Tuning
Chen Jin, Ryutaro Tanno, Amrutha Saseendran +2
We introduce Lavender, a simple supervised fine-tuning (SFT) method that boosts the performance of advanced vision-language models (VLMs) by leveraging state-of-the-art image gener…
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal +5
Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowle…
Tackling Structural Hallucination in Image Translation with Local Diffusion
Seunghoi Kim, Chen Jin, Tom Diethe +5
Recent developments in diffusion models have advanced conditioned image generation, yet they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in…
Improving Antibody Humanness Prediction using Patent Data
Talip Ucar, Aubin Ramon, Dino Oglic +3
We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process. Humanness serves as a proxy for the im…
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
Chen Jin, Ryutaro Tanno, Amrutha Saseendran +2
Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural l…
Unlocking the Heart Using Adaptive Locked Agnostic Networks
Sylwia Majchrowska, Anders Hildeman, Philip Teare +1
Supervised training of deep learning models for medical imaging applications requires a significant amount of labeled data. This is posing a challenge as the images are required to…