Datasets:
Tasks:
Visual Question Answering
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 4,638 Bytes
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license: mit
license_name: mixed-see-licensing-section
language:
- en
pretty_name: OPTICS
task_categories:
- visual-question-answering
size_categories:
- 1K<n<10K
tags:
- commonsense-reasoning
- object-properties
- counting
- vision-language-models
- benchmark
configs:
- config_name: OPTICS-CNT
data_dir: OPTICS-CNT/data
- config_name: OPTICS-CMP
data_dir: OPTICS-CMP/merged_images
---
<h1 align="center">OPTICS: Commonsense Reasoning over Visual Object Properties</h1>
<div align="center">
Inspired by human object categorization, OPTICS (formerly ORBIT) is a benchmark suite for evaluating how Vision–Language Models (VLMs) perceive and reason about object properties. It spans four object property dimensions (physical, taxonomic, functional, relational), three levels of reasoning complexity (direct recognition, property inference, counterfactual reasoning), and three image types (photographic/real, animated, AI-generated). It has two parts: **OPTICS-CNT** (exact counting) and **OPTICS-CMP** (count comparison).
📄 [Paper](https://arxiv.org/pdf/2508.10956) |
📚 [arXiv](https://arxiv.org/abs/2508.10956) |
💻 [Code](https://github.com/AbhishekKolari/OPTICS)
</div>
### Uses
This dataset tests VLMs with count-based questions to evaluate how well they reason about object properties across visual contexts. Load each part as its own config:
```python
from datasets import load_dataset
cnt = load_dataset("Abk802/OPTICS", "OPTICS-CNT", split="train")
cmp = load_dataset("Abk802/OPTICS", "OPTICS-CMP", split="train")
```
### Repository structure
```
OPTICS-CNT/
├── data/ # images + metadata.csv per image type
└── <benchmark>.json
OPTICS-CMP/
├── merged_images/ # merged image pairs + metadata.csv
└── <merged_benchmark>.json
```
### OPTICS-CNT
360 images (120 per image type) with 1,080 counting questions, 3 per image, one per reasoning level. Ground-truth counts range from 0 to 10.
Fields:
- `image_id`: the image number (120 under each `image_type`).
- `image_type`: `REAL`, `ANIMATED` or `AI_GENERATED`.
- `theme`: label indicating the image theme.
- `reasoning_complexity`: `direct_recognition`, `property_inference` or `counterfactual_reasoning`.
- `question_id`: the question number (one per reasoning level).
- `question`: the counting question.
- `answer`: the ground-truth count.
- `property_category`: `physical`, `taxonomic`, `functional` or `relational`.
- `objects`: annotator notes on the counted objects (reasoning for `answer`; not given to models).
### OPTICS-CMP
2,122 comparison questions built by pairing semantically similar OPTICS-CNT questions (cosine similarity ≥ 0.80). Each example is a left/right concatenation of two images, and the model decides which side has the larger count. Equal-count pairs are excluded, and count differences range from 1 to 9.
Fields:
- `question`: the comparison question ("Let A be the answer to … for the left image. Let B be the answer to … for the right image. Which is greater, A or B?").
- `answer`: `A` or `B` (evaluation label).
- `answer_side`: `LEFT` or `RIGHT`.
- `property_category`, `reasoning_complexity`: category of the paired questions.
- `count_diff`: absolute difference between the two ground-truth counts.
- `question1`, `image_id1`, `image_type1`, `count1`: left-hand question, image and count.
- `question2`, `image_id2`, `image_type2`, `count2`: right-hand question, image and count.
- `similarity`: cosine similarity of the paired question embeddings.
### Licensing
- Code and annotations (questions and answers): MIT License.
- Images remain subject to their source licenses:
- **Unsplash:** Unsplash License (free to use, modify and redistribute, no attribution required).
- **Freepik:** Freepik Free License (free tier, attribution required).
- **Google Images:** used only as a search interface. Most images were traced to Unsplash or Freepik. Fewer than 10 untraceable images are used solely for non-commercial research benchmarking.
- **AI-generated images** (GPT-4o, Grok 3): generated by the authors, subject to OpenAI and xAI usage terms.
### Citation
```bibtex
@misc{kolari2026studycommonsensereasoningvisual,
title={A Study of Commonsense Reasoning over Visual Object Properties},
author={Abhishek Kolari and Mohammadhossein Khojasteh and Yifan Jiang and Floris den Hengst and Filip Ilievski},
year={2026},
eprint={2508.10956},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.10956},
}
```
<!-- Results in the paper use dataset version `v1.0` (GitHub commit `95dbde9`). --> |