Double-Filter: Efficient Fine-tuning of Pre-trained Vision-Language Models via Patch&Layer Filtering
No confident code implementation found for this paper. 5 possible matches can be shown below.
No confident code implementation found for this paper. 5 possible matches can be shown below.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation
Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
BayesTune: Bayesian Sparse Deep Model Fine-tuning
Connecting Pre-trained Language Model and Downstream Task via Properties of Representation
@article{he2026doublefilter,
title = {Double-Filter: Efficient Fine-tuning of Pre-trained Vision-Language Models via Patch&Layer Filtering},
author = {Yaoqin He and Junchen Fu and Kaiwen Zheng and Songpei Xu and Fuhai Chen and Jie Li and Joemon M. Jose and Xuri Ge},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:402c174faad5d50537e98ec456fdf8d5f853229a},
journal = {ICML 2025 2025}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/402c174faad5d50537e98ec456fdf8d5f853229a)