3 papers
q-bio.QM2025
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…
cs.CV2025
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…
cs.LG2025
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…