Nghia Vu — accepted AI-conference papers

2026

AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers

Nghia Vu, Tuong Do, Khang Nguyen, Baoru Huang, Nhat Le, Binh Xuan Nguyen +5

CVPR 2026

Affordance learning is a complex challenge in many applications, where existing approaches primarily focus on the geometric structures, visual knowledge, and affordance labels of objects to determine interactable regions. However, extending this learning capability to a scene is significantly more c

Cited by 0 Source ↗code ↗Scholar ↗

2025

FedEFM: Federated Endovascular Foundation Model with Unseen Data

Tuong Do, Nghia Vu, Tudor Jianu, Baoru Huang, Minh Vu, Jionglong Su +4

ICRA 2025

In endovascular surgery, the precise identification of catheters and guidewires in X-ray images is essential for reducing intervention risks. However, accurately segmenting catheter and guidewire structures is challenging due to the limited availability of labeled data. Foundation models offer a pro

Cited by 3 Source ↗Scholar ↗