Cover Image

An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Paul, Sneha, Patterson, Zachary, Bouguila, Nizar
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data
by: Paul, Sneha, et al.
Published: (2024)

Point Cloud as a Foreign Language for Multi-modal Large Language Model
by: Paul, Sneha, et al.
Published: (2026)

PatchSVD: A Non-uniform SVD-based Image Compression Algorithm
by: Golpayegani, Zahra, et al.
Published: (2024)

LuKAN: A Kolmogorov-Arnold Network Framework for 3D Human Motion Prediction
by: Hasan, Md Zahidul, et al.
Published: (2025)

Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness
by: Golpayegani, Zahra, et al.
Published: (2024)

Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models
by: Marti-Escofet, Francesc, et al.
Published: (2025)

Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models
by: Jiang, Yu, et al.
Published: (2026)

Fine-Grained VLM Fine-tuning via Latent Hierarchical Adapter Learning
by: Zhao, Yumiao, et al.
Published: (2025)

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation
by: Ye, Yiwen, et al.
Published: (2025)

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis
by: Li, Mengke, et al.
Published: (2025)

Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning
by: Wang, Zhaozhi, et al.
Published: (2024)

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter
by: Jiang, Bo, et al.
Published: (2025)

Pear: Pruning and Sharing Adapters in Visual Parameter-Efficient Fine-Tuning
by: Zhong, Yibo, et al.
Published: (2024)

Semantic-guided Fine-tuning of Foundation Model for Long-tailed Visual Recognition
by: Peng, Yufei, et al.
Published: (2025)

Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?
by: Li, Jiawen, et al.
Published: (2025)

Enhancing Rotation-Invariant 3D Learning with Global Pose Awareness and Attention Mechanisms
by: Guo, Jiaxun, et al.
Published: (2025)

Robust Fine-tuning for Pre-trained 3D Point Cloud Models
by: Zhang, Zhibo, et al.
Published: (2024)

PanAdapter: Two-Stage Fine-Tuning with Spatial-Spectral Priors Injecting for Pansharpening
by: Wu, RuoCheng, et al.
Published: (2024)

Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision
by: Li, Minglei, et al.
Published: (2024)

Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing Label Bias in Foundation Models
by: Zhu, Beier, et al.
Published: (2023)

Supervised Fine-tuning in turn Improves Visual Foundation Models
by: Jiang, Xiaohu, et al.
Published: (2024)

Improvise, Adapt, Overcome -- Telescopic Adapters for Efficient Fine-tuning of Vision Language Models in Medical Imaging
by: Mishra, Ujjwal, et al.
Published: (2025)

Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference
by: Liu, Ting, et al.
Published: (2024)

TASeg: Text-aware RGB-T Semantic Segmentation based on Fine-tuning Vision Foundation Models
by: Yu, Meng, et al.
Published: (2025)

Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
by: Lian, Chenyu, et al.
Published: (2024)

ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation
by: Pu, Xinyang, et al.
Published: (2024)

EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection
by: Nakamura, Hiroki, et al.
Published: (2026)

ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation
by: Shi, Ruohua, et al.
Published: (2024)

Post-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Models
by: Amini, Kooshan, et al.
Published: (2025)

Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification
by: Ligan, Bernardin, et al.
Published: (2025)

ULFine: Unbiased Lightweight Fine-tuning for Foundation-Model-Assisted Long-Tailed Semi-Supervised Learning
by: Zhang, Enhao, et al.
Published: (2025)

PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters
by: Yu, Yinghong, et al.
Published: (2026)

Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter
by: Zhong, Weizhi, et al.
Published: (2025)

HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter
by: Zhao, Yumiao, et al.
Published: (2024)

Robust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
by: Jiang, Bo, et al.
Published: (2025)

IP-Adapter Is All You Need: Towards Fine-Tuning-Free Diffusion-Based Talking Face Generation
by: Wu, Hao, et al.
Published: (2026)

iDAT: inverse Distillation Adapter-Tuning
by: Ruan, Jiacheng, et al.
Published: (2024)

Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes
by: Xiao, Yujie, et al.
Published: (2025)

Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models
by: Long, Jiahuan, et al.
Published: (2025)

Exploiting Layer Normalization Fine-tuning in Visual Transformer Foundation Models for Classification
by: Tan, Zhaorui, et al.
Published: (2025)