[ICCV2025] MCP

Official Implementation of "MCP: Multi-Cache Enhanced Prototype Learning for Test-Time Generalization of Vision-Language Models" (ICCV 2025)
🔗 Read our paper: arxiv
🌐 Project page: MCP-ICCV25

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Overview

🚀 This repository contains the official implementation of MCP (Multi-Cache Enhanced Prototype Learning), proposed in our ICCV 2025 paper.
MCP introduces a multi-cache mechanism that dynamically maintains prototype representations during test-time adaptation (TTA) of vision-language models (VLMs), enabling robust generalization under distribution shifts.

Abstract: In the zero-shot setting, test-time adaptation adjusts pre-trained models using unlabeled data from the test phase to enhance performance on unknown test distributions. Existing cache-enhanced TTA methods rely on a low-entropy criterion to select samples for prototype construction, assuming intra-class compactness. However, low-entropy samples may be unreliable under distribution shifts, and the resulting prototypes may not ensure compact intra-class distributions. This study identifies a positive correlation between cache-enhanced performance and intra-class compactness. Based on this observation, we propose a Multi-Cache enhanced Prototype-based Test-Time Adaptation (MCP) featuring three caches: an entropy cache for initializing prototype representations with low-entropy samples, an align cache for integrating visual and textual information to achieve compact intra-class distributions, and a negative cache for prediction calibration using high-entropy samples. We further develop MCP++, a framework incorporating cross-modal prototype alignment and residual learning, introducing prototype residual fine-tuning. Comparative and ablation experiments across 15 downstream tasks demonstrate that the proposed method and framework achieve state-of-the-art generalization performance.

Installation

git clone https://github.com/CenturyChen/MCP.git
cd MCP
conda create -n mcp python=3.10
conda activate mcp
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Dataset

📦 For dataset preparation, please follow the instructions provided in DATASETS.md.
Our implementation follows the same dataset setup protocol as TDA, covering the two standard benchmarks used in test-time adaptation.

Run MCP

OOD Benchmark

• ResNet50: Run MCP on the OOD Benchmark using the ResNet50 model:

bash ./scripts/run_ood_benchmark_rn50.sh 

• ViT-B/16: Run MCP on the OOD Benchmark using the ViT-B/16 model:

bash ./scripts/run_ood_benchmark_vit.sh 

Cross-Domain Benchmark

• ResNet50: Run MCP on the Cross-Domain Benchmark using the ResNet50 model:

bash ./scripts/run_cd_benchmark_rn50.sh 

• ViT-B/16: Run MCP on the Cross-Domain Benchmark using the ViT-B/16 model:

bash ./scripts/run_cd_benchmark_vit.sh 

Citation

🤗 If you find our work useful, please consider citing:

@inproceedings{chen2025multi,
  title={Multi-Cache Enhanced Prototype Learning for Test-Time Generalization of Vision-Language Models},
  author={Chen, Xinyu and Zhai, Haotian and Zhang, Can and Shi, Xiupeng and Li, Ruirui},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2281--2291},
  year={2025}
}

Acknowledgements

This research is inspired by previous works including Tip-Adapter, TPT, CuPL, TDA, and DPE-CLIP. We appreciate their valuable open-source efforts.