18 citations · 24 across the 13 of their papers we have counts for
13 papers
FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology
Anh-Tien Nguyen, Trung DQ. Dang, Nghiem Tuong Diep +9
Pathology vision-language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high cost of expert pathology anno…
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
Minh-Loi Nguyen, Nghiem T. Diep, Nghiem Tuong Diep +19
Robot world models now generate synthetic manipulation videos, but evaluating them is hard: realistic-looking outputs often violate physical laws, temporal consistency or task logi…
FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen +20
Vision-Language-Action (VLA) models enable general-purpose robotic control via large-scale multimodal pretraining, yet their effectiveness under few-shot imitation learning remains…
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…
DoRAN: Stabilizing Weight-Decomposed Low-Rank Adaptation via Noise Injection and Auxiliary Networks
Nghiem T. Diep, Hien Dang, Tuan Truong +3
Parameter-efficient fine-tuning (PEFT) methods have become the standard paradigm for adapting large-scale models. Among these techniques, Weight-Decomposed Low-Rank Adaptation (DoR…
Hypernetwork-Driven Low-Rank Adaptation Across Attention Heads
Nghiem T. Diep, Dung Le, Tuan Truong +3
Parameter-efficient fine-tuning (PEFT) has emerged as a powerful paradigm for adapting large-scale pre-trained models to downstream tasks with minimal additional parameters. Among…
MELCOT: A Hybrid Learning Architecture with Marginal Preservation for Matrix-Valued Regression
Khang Tran, Hieu Cao, Thinh Pham +3
Regression is essential across many domains but remains challenging in high-dimensional settings, where existing methods often lose spatial structure or demand heavy storage. In th…
MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification
Anh-Tien Nguyen, Duy Minh Ho Nguyen, Nghiem Tuong Diep +7
Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels, hindering model generalization. This paper introduces a p…
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
Nghiem T. Diep, Huy Nguyen, Chau Nguyen +5
The LLaMA-Adapter has recently emerged as an efficient fine-tuning technique for LLaMA models, leveraging zero-initialized attention to stabilize training and enhance performance.…
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…
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model
Duy M. H. Nguyen, An T. Le, Trung Q. Nguyen +7
Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and mi…
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…
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep +9
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained dee…