Research Paper#Vision Transformers, Fine-tuning, Low-Rank Adaptation, Point Cloud Analysis🔬 ResearchAnalyzed: Jan 3, 2026 06:29
CLoRA: Efficient Vision Transformer Fine-tuning
Published:Dec 31, 2025 03:46
•1 min read
•ArXiv
Analysis
This paper introduces CLoRA, a novel method for fine-tuning pre-trained vision transformers. It addresses the trade-off between performance and parameter efficiency in existing LoRA methods. The core idea is to share base spaces and enhance diversity among low-rank modules. The paper claims superior performance and efficiency compared to existing methods, particularly in point cloud analysis.
Key Takeaways
- •Proposes CLoRA, a new fine-tuning method for Vision Transformers.
- •Employs base-space sharing and sample-agnostic diversity enhancement (SADE).
- •Aims to balance performance and parameter efficiency.
- •Demonstrates superior performance, especially in point cloud analysis.
- •Requires fewer GFLOPs compared to state-of-the-art methods.
Reference
“CLoRA strikes a better balance between learning performance and parameter efficiency, while requiring the fewest GFLOPs for point cloud analysis, compared with the state-of-the-art methods.”