activity
20232026
most citedAdvancing Wound Filling Extraction on 3D Faces: Auto-Segmentation and Wound Face Regeneration Approach

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2026

WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses

Thinh D. Le, Son T. Nguyen, Duong Q. Nguyen +3

Vision-Language-Action (VLA) models have achieved impressive results in robotic manipulation and ground-mobile navigation, yet language-conditioned control of unmanned aerial vehic…

cs.CV2025

Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing

Luu Tung Hai, Thinh D. Le, Zhicheng Ding +2

Point cloud processing has gained significant attention due to its critical role in applications such as autonomous driving and 3D object recognition. However, deploying high-perfo…

cs.CL2025

ReZero: Enhancing LLM search ability by trying one-more-time

Alan Dao, Thinh Le

Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM) performance on knowledge-intensive tasks but depends heavily on initial search query quality. Current metho…

cs.CV2023★ 2 cited

Advancing Wound Filling Extraction on 3D Faces: Auto-Segmentation and Wound Face Regeneration Approach

Duong Q. Nguyen, Thinh D. Le, Phuong D. Nguyen +2

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient appr…

cs.CV2023

Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures

Phuong D. Nguyen, Thinh D. Le, Duong Q. Nguyen +2

In this study, we emphasize the integration of a pre-trained MICA model with an imperfect face dataset, employing a self-supervised learning approach. We present an innovative meth…

cs.CV2023

3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes

Phuong D. Nguyen, Thinh D. Le, Duong Q. Nguyen +3

This study explores the potential of a fully convolutional mesh autoencoder model for regenerating 3D nature faces with the presence of imperfect areas. We utilize deep learning ap…