4 papers
Domain Generalizable Adaptation of 3D Vision-Language Models via Regularized Fine-Tuning
Sneha Paul, Zachary Patterson, Nizar Bouguila
Domain adaptation remains a central challenge in 3D vision, especially for multimodal foundation models that align 3D point clouds with visual and textual data. While these models…
An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models
Sneha Paul, Zachary Patterson, Nizar Bouguila
Point cloud foundation models demonstrate strong generalization, yet adapting them to downstream tasks remains challenging in low-data regimes. Full fine-tuning often leads to over…
Point Cloud as a Foreign Language for Multi-modal Large Language Model
Sneha Paul, Zachary Patterson, Nizar Bouguila
Multi-modal large language models (MLLMs) have shown remarkable progress in integrating visual and linguistic understanding. Recent efforts have extended these capabilities to 3D u…
Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data
Sneha Paul, Zachary Patterson, Nizar Bouguila
Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Tradi…