VLM-SubtleBench

VLM-SubtleBench: How Far Are VLMs from Human-Level Subtle Comparative Reasoning?

The ability to distinguish subtle differences between visually similar images is essential for diverse domains such as industrial anomaly detection, medical imaging, and aerial surveillance. While comparative reasoning benchmarks for vision-language models (VLMs) have recently emerged, they primarily focus on images with large, salient differences and fail to capture the nuanced reasoning required for real-world applications.

VLM-SubtleBench is a benchmark designed to evaluate VLMs on subtle comparative reasoning — detecting fine-grained differences between highly similar image pairs that are easy for humans but challenging for state-of-the-art VLMs. Unlike prior benchmarks restricted to natural image datasets, VLM-SubtleBench spans diverse domains including industrial, aerial, and medical imagery.

Benchmark Summary

Total QA pairs	12,923
Difference types	10
Image domains	6 (Natural, Industrial, Aerial, Synthetic, Medical)
Data sources	14
Human captions	1,200
Splits	test (11,688) / val (1,235)
Task format	Multiple-choice VQA + Image Difference Captioning

Note: Medical domain images (MIMIC-CXR, 362 pairs) are not included due to licensing restrictions, but their QA entries are included in qa.json. See Medical Data below for instructions on how to obtain the images.

Medical Data (MIMIC-CXR)

The medical domain QA entries (362 attribute comparison pairs from MIMIC-CXR chest X-rays, 664 unique images) are included in qa.json, but the corresponding images are not included due to PhysioNet licensing requirements.

Step 1: Obtain PhysioNet Credentialed Access

1. Create an account at PhysioNet
2. Complete the required CITI training course for "Data or Specimens Only Research"
3. Go to MIMIC-CXR-JPG v2.1.0 and sign the data use agreement
4. Wait for your access to be approved

Step 2: Download Images

We provide a script that automatically downloads only the 664 images required by qa.json and places them at the expected paths (images/mimic/...).

python download_mimic.py --user <physionet-username> --password <physionet-password>

The script:

• Parses qa.json to find all required MIMIC-CXR image paths
• Downloads each image from PhysioNet via wget
• Places them under images/mimic/ preserving the original directory hierarchy (e.g., images/mimic/p15/p15592981/s55194630/{hash}.jpg)
• Skips images that already exist, so it is safe to re-run

You can also download individual images manually:

wget --user <username> --password <password> \
  https://physionet.org/files/mimic-cxr-jpg/2.1.0/files/p15/p15000170/s54385701/3ea0cd5d-b6ef4a9d-bd053deb-a611067c-284e4144.jpg \
  -O images/mimic/p15/p15000170/s54385701/3ea0cd5d-b6ef4a9d-bd053deb-a611067c-284e4144.jpg

Download and Evaluation

Download

# Using huggingface_hub
pip install huggingface_hub

python -c "from huggingface_hub import snapshot_download; snapshot_download('KRAFTON/VLM-SubtleBench', repo_type='dataset', local_dir='VLM-SubtleBench')"

Or clone directly with Git LFS:

git lfs install
git clone https://huggingface.co/datasets/KRAFTON/VLM-SubtleBench

Evaluation

For evaluation code and instructions, please refer to the official GitHub repository:

https://github.com/krafton-ai/VLM-SubtleBench

Citation

To be added.