3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2026
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…