2 citations · 2 across the 2 of their papers we have counts for
6 papers
TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving
Quang-Huy Che, Duc-Tri Le, Minh-Quan Pham +2
Semantic segmentation is a fundamental perception task in autonomous driving, particularly for identifying drivable areas and lane markings to enable safe navigation. However, most…
A Re-ranking Method using K-nearest Weighted Fusion for Person Re-identification
Huy Che, Le-Chuong Nguyen, Gia-Nghia Tran +2
In person re-identification, re-ranking is a crucial step to enhance the overall accuracy by refining the initial ranking of retrieved results. Previous studies have mainly focused…
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Quang-Huy Che, Duc-Khai Lam
Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiven…
Enhanced Generative Data Augmentation for Semantic Segmentation via Stronger Guidance
Quang-Huy Che, Duc-Tri Le, Bich-Nga Pham +2
Data augmentation is crucial for pixel-wise annotation tasks like semantic segmentation, where labeling requires significant effort and intensive labor. Traditional methods, involv…
Enhancing person re-identification via Uncertainty Feature Fusion Method and Auto-weighted Measure Combination
Quang-Huy Che, Le-Chuong Nguyen, Duc-Tuan Luu +1
Person re-identification (Re-ID) is a challenging task that involves identifying the same person across different camera views in surveillance systems. Current methods usually rely…
FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement
Gia-Nghia Tran, Quang-Huy Che, Trong-Tai Dam Vu +4
Generating multiple new concepts remains a challenging problem in the text-to-image task. Current methods often overfit when trained on a small number of samples and struggle with…