4 citations · 7 across the 6 of their papers we have counts for
6 papers
From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models
Zanyi Wang, Xin Lin, Haodong Li +2
Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing g…
StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models
Duy M. H. Nguyen, Tuan A. Tran, Duong Nguyen +17
Recent token merging techniques for Vision Transformers (ViTs) provide substantial speedups by reducing the number of tokens processed by self-attention, often without retraining.…
S-Chain: Structured Visual Chain-of-Thought For Medicine
Khai Le-Duc, Duy M. H. Nguyen, Phuong T. H. Trinh +21
Faithful reasoning in medical vision-language models (VLMs) requires not only accurate predictions but also transparent alignment between textual rationales and visual evidence. Wh…
ExGra-Med: Extended Context Graph Alignment for Medical Vision-Language Models
Duy M. H. Nguyen, Nghiem T. Diep, Trung Q. Nguyen +10
State-of-the-art medical multi-modal LLMs (med-MLLMs), such as LLaVA-Med and BioMedGPT, primarily depend on scaling model size and data volume, with training driven largely by auto…
Accelerating Transformers with Spectrum-Preserving Token Merging
Hoai-Chau Tran, Duy M. H. Nguyen, Duy M. Nguyen +7
Increasing the throughput of the Transformer architecture, a foundational component used in numerous state-of-the-art models for vision and language tasks (e.g., GPT, LLaVa), is an…
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation
Duy Minh Ho Nguyen, Tan Ngoc Pham, Nghiem Tuong Diep +9
Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundatio…