Datasets:

KRAFTON
/

VLM-SubtleBench

license: cc-by-nc-4.0
task_categories:
  - visual-question-answering
  - image-to-text
language:
  - en
tags:
  - vlm
  - benchmark
  - comparative-reasoning
  - subtle-difference
  - image-comparison
  - multi-image
size_categories:
  - 10K<n<100K

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.

Key Findings

Proprietary VLMs still struggle with subtle visual comparison, leaving large gaps from human performance (best model: 77.8% vs. human: 95.5%).
Simple prompting strategies such as chain-of-thought, grid layouts, and overlapping images yield only limited improvements.
VLMs are highly sensitive to difficulty factors such as object size and count, with performance degrading sharply as scene complexity increases.
Explicit reasoning helps: models with stronger inherent reasoning capabilities achieve higher accuracy across all difference types.

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.

Difference Types

VLM-SubtleBench covers 10 representative difference types, spanning from low-level visual variations to high-level semantic changes:

Category	Description	Count
Attribute	Variations in object properties (color, size, shape)	1,196
State	Object condition changes (breakage, cracks, peeling)	1,148
Emotion	Comparative judgments of facial expression intensity	1,108
Temporal	Identifying which image depicts an earlier/later event	1,117
Spatial	Changes in arrangement or relative position	1,235
Existence	Whether an object has appeared or disappeared	1,204
Quantity	Changes in object count	1,599
Quality	Degradations such as blur, noise, or overexposure	1,109
Viewpoint	Camera perspective changes (pan, tilt, roll, orbit)	1,734
Action	Differences in human/object poses or activities	1,111

Image Domains and Sources

Domain	Count	Sources
Natural	7,526	CameraBench, ChangeIt, COCO, CREMA-D/RAVDESS/AFEW-VA/DAiSEE, MegaFruits, UCF-QNRF, VLM4D, YouTube-8M
Synthetic	3,190	Procedurally generated primitive scenes (circles, squares, triangles on white backgrounds)
Industrial	1,202	MVTec-AD, MVTec-LOCO
Aerial	643	LEVIR-MCI, UBC
Medical	362*	MIMIC-CXR (not included, see below)

Dataset Structure

VLM-SubtleBench/
├── README.md
├── qa.json                          # All QA pairs with metadata (including MIMIC)
└── images/
    ├── camerabench/                 # CameraBench — viewpoint
    ├── changeit/                    # ChangeIt — state, quantity
    │   ├── state/
    │   └── quantity/
    ├── coco/                        # COCO + Gemini edits — attribute, existence
    │   ├── val2017/
    │   ├── val2017_attribute_edit/
    │   ├── val2017_existence_edit/
    │   ├── train2017/
    │   ├── train2017_attribute_edit/
    │   └── train2017_existence_edit/
    ├── emotion_videos/              # CREMA-D/RAVDESS/AFEW-VA/DAiSEE — emotion
    ├── levir/                       # LEVIR-MCI — existence, quantity
    │   ├── existence/
    │   └── quantity/
    ├── megafruits/                   # MegaFruits — quantity
    ├── mvtec_ad/                    # MVTec-AD — attribute, state
    ├── mvtec_loco/                  # MVTec-LOCO — quantity
    ├── synthetic/                   # Synthetic primitives — attribute, existence, quantity, spatial, viewpoint
    │   ├── attribute/
    │   ├── existence/
    │   ├── quantity/
    │   ├── spatial/
    │   └── viewpoint/
    ├── ubc/                         # UBC — quantity
    ├── ucf_qnrf/                    # UCF-QNRF — quantity
    ├── vlm4d/                       # VLM4D — spatial, temporal
    │   ├── spatial/
    │   └── temporal/
    └── yt8m/                        # YouTube-8M — action, quality, temporal
        ├── action/
        ├── quality/
        └── temporal/

QA Entry Format

Each entry in qa.json:

{
  "image_1": "images/camerabench/processed_frames/1018.1.7/frame_1.png",
  "image_2": "images/camerabench/processed_frames/1018.1.7/frame_2.png",
  "question": "In which direction does the camera move from the first image to the second image?",
  "answer": "backward",
  "distractors": ["forward"],
  "has_caption": false,
  "caption": null,
  "split": "test",
  "metadata": {
    "category": "viewpoint",
    "domain": "natural",
    "source": "camerabench",
    "source_id": "0",
    "raw_folder": "camera_pairs",
    "generation_info": {
      "movement_type": "dolly-out",
      "original_labels": ["minimal-shaking", "complex-motion", "regular-speed", "dolly-out", "lead-tracking"],
      "video_path": "videos_gif/1018.1.7.gif"
    }
  }
}

Field	Description
`image_1`, `image_2`	Relative paths to the image pair
`question`	The comparative question about the two images
`answer`	Correct answer
`distractors`	Incorrect answer choices
`has_caption`	Whether a human-written difference caption is available
`caption`	Human-written description of the difference between the images (null if unavailable)
`split`	`test` or `val`
`metadata.category`	One of the 10 difference types
`metadata.domain`	Image domain (natural, industrial, aerial, synthetic, medical)
`metadata.source`	Source dataset identifier
`metadata.source_id`	Original ID within the source dataset
`metadata.generation_info`	Source-specific metadata (varies by source, may be null)

Medical Data (MIMIC-CXR)

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

To obtain the medical images:

Obtain credentialed access to MIMIC-CXR-JPG v2.1.0 on PhysioNet
Download the required chest X-ray images
Place them under images/mimic/ following the path structure referenced in qa.json (e.g., images/mimic/p15/p15592981/s55194630/{hash}.jpg)

The image paths preserve the original MIMIC-CXR directory hierarchy, so files can be copied directly from a standard MIMIC-CXR-JPG download.

Loading and Evaluation

To be added.

Citation

To be added.