microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification

📢 Latest Updates

• April-07-26: Our paper has been accepted at ACL 2026 Findings!
• Oct-03-25: Preprint available on arXiv.
• Oct-03-25: Initial release of microCLIP code.

Table of Contents

• 💡 Overview
• 📊 Results
• 📦 Installation
• 🗂️ Datasets
• 🔧 Usage
• 🙏 Acknowledgements
• 📜 Citation

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💡 Overview

microCLIP is a lightweight self-training framework that adapts CLIP for fine-grained image classification without requiring labeled data.

While CLIP is strong in zero-shot transfer, it primarily relies on coarse global features. microCLIP enhances CLIP with localized, fine-grained cues, enabling sharper attention, more accurate pseudo-labels, and improved classification accuracy across challenging benchmarks.

Key ideas:

• Saliency-Oriented Attention Pooling (SOAP): builds a fine-grained [FG] token from salient patch embeddings.
• TokenFusion: fuses [FG] with the global [CLS] token for coarse–fine alignment.
• Two-headed LLM-derived classifier: a frozen prior and a learnable classifier stabilize pseudo-labeling.
• Dynamic Knowledge Aggregation: convexly combines static CLIP/LLM priors with evolving TokenFusion logits.

microCLIP improves +2.90% average accuracy across 13 fine-grained benchmarks, setting a new state-of-the-art for unsupervised CLIP adaptation.

Overall Architecture

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📊 Results

Comparison to Zero-shot and UA Baselines

Ablation Studies

Effect of coarse vs fine-grained cues:

Effect of SOAP:

Dynamic Knowledge Aggregation:

Two-headed classifier initialization:

Backbone Scaling

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Visualizations

Sharper local attention via SOAP-guided [FG]:

[CLS] vs [FG] attention across datasets:

Pseudo-label accuracy progression:

NCut saliency masks:

📦 Installation

# Clone repository
git clone https://github.com/sathiiii/microCLIP.git
cd microCLIP

# Create environment
conda env create -f environment.yml
conda activate microclip

🗂️ Datasets

• Dataset paths are defined in configs/dataset_catalog.json. You will need to update these paths to point to your local dataset locations.

• Dataset label files are provided in configs/classes.json.

• For dataset preparation, we recommend using the scripts from the VISSL repository.

• Check this issue for guidance on downloading Stanford Cars dataset.

🔧 Usage

Train (UA Fine-tuning)

python train.py --dataset dataset-name --train_config ours_vit_b_32_cupl_proto

Evaluate

python evaluate.py --dataset dataset-name --ckpt-path path/to/checkpoint.pth

🙏 Acknowledgements

This work builds upon the MUST repository. We thank the authors for their open-source code.

We thank the authors of MetaCLIP for releasing their codebase, which we use in our additional experiments.

We also acknowledge CuPL for providing GPT-3 generated class descriptions, which we include in our repository under all_prompts/.

📜 Citation

If you find this work useful in your research, please consider citing:

@misc{silva2025microclipunsupervisedclipadaptation,
      title={microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification}, 
      author={Sathira Silva and Eman Ali and Chetan Arora and Muhammad Haris Khan},
      year={2025},
      eprint={2510.02270},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.02270}, 
}