STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification (CVPR 2025)

Siyi Du, Xinzhe Luo, Declan P. O'Regan, and Chen Qin

Overall framework of STiL. STiL encodes image-tabular data using $\phi$, decomposes modality-shared and -specific information through DCC $\psi$ (a), and outputs predictions via multimodal and unimodal classifiers $f$. STiL generates pseudo-labels for unlabeled data using CGPL (b) and refines them with prototype similarity scores in PGLS (c). (d) Training pathways for labeled and unlabeled data.

This is an official PyTorch implementation for STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification. We built the code based on siyi-wind/TIP.

We also include plenty of comparing models in this repository: SimMatch, Multimodal SimMatch, CoMatch, Multimodal CoMatch, FreeMatch, Multimodal FreeMatch, MMatch, and Co-training (Please go to the paper to find the detailed information of these models).

Concact: [email protected] (Siyi Du)

Share us a ⭐ if this repository does help.

Updates

[12/03/2025] The arXiv paper and the code are released.

Contents

• Requirements
• Data Preparation
• Training & Testing
• Checkpoints
• Lisence & Citation
• Acknowledgements

Requirements

This code is implemented using Python 3.9.15, PyTorch 1.11.0, PyTorch-lighting 1.6.4, CUDA 11.3.1, and CuDNN 8.

cd STiL/
conda env create --file environment.yaml
conda activate stil

Data

Download DVM data from here

Apply for the UKBB data here

Preparation

We conduct the same data preprocessing process as siyi-wind/TIP.

Training

Training

CUDA_VISIBLE_DEVICES=0 python -u run.py --config-name config_dvm_STiL dataset=dvm_all_server_reordered_SemiPseudo_0.01 exp_name=train evaluate=True checkpoint={YOUR_PRETRAINED_CKPT_PATH}

Testing

CUDA_VISIBLE_DEVICES=0 python -u run.py --config-name config_dvm_STiL dataset=dvm_all_server_reordered_SemiPseudo_0.01 exp_name=test test=True checkpoint={YOUR_TRAINED_CKPT_PATH}

Checkpoints

Task	1% labeled	10% labeled
Car model prediction (DVM)	Download	Download
CAD classification (Cardiac)	Download	Download
Infarction classification (Cardiac)	Download	Download

Lisence & Citation

This repository is licensed under the Apache License, Version 2.

If you use this code in your research, please consider citing:

@inproceedings{du2025stil,
  title={{STiL}: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification},
  author={Du, Siyi and Luo, Xinzhe and O'Regan, Declan P. and Qin, Chen},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR) 2025},
  year={2025}}

Acknowledgements

We would like to thank the following repositories for their great works:

• TIP
• PIBD
• MMCL
• BLIP