1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
Catch Me If You Can Describe Me: Open-Vocabulary Camouflaged Instance Segmentation with Diffusion
Tuan-Anh Vu, Duc Thanh Nguyen, Qing Guo +4
Text-to-image diffusion techniques have shown exceptional capabilities in producing high-quality, dense visual predictions from open-vocabulary text. This indicates a strong correl…
Test-Time Augmentation for 3D Point Cloud Classification and Segmentation
Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang +2
Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shap…
Advances in 3D Neural Stylization: A Survey
Yingshu Chen, Guocheng Shao, Ka Chun Shum +2
Modern artificial intelligence offers a novel and transformative approach to creating digital art across diverse styles and modalities like images, videos and 3D data, unleashing t…
Locally Stylized Neural Radiance Fields
Hong-Wing Pang, Binh-Son Hua, Sai-Kit Yeung
In recent years, there has been increasing interest in applying stylization on 3D scenes from a reference style image, in particular onto neural radiance fields (NeRF). While perfo…
Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates
Ka Chun Shum, Jaeyeon Kim, Binh-Son Hua +2
Neural radiance field is an emerging rendering method that generates high-quality multi-view consistent images from a neural scene representation and volume rendering. Although neu…