---
pretty_name: HVSBench
language:
- en
task_categories:
- visual-question-answering
- image-classification
tags:
- multimodal
- vision-language
- human-visual-system
- visual-saliency
- scanpath
- benchmark
license: other
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: index
    dtype: int64
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: question_type
    dtype: string
  - name: answer
    dtype: string
  - name: answer_type
    dtype: string
  - name: category
    dtype: string
  - name: split
    dtype: string
  - name: real_image_path
    dtype: string
  - name: origin_dataset
    dtype: string
  splits:
  - name: test
    num_bytes: 2041995103
    num_examples: 8657
  download_size: 1991106611
  dataset_size: 2041995103
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# HVSBench

HVSBench is a benchmark for evaluating how well multimodal large language models align with human perceptual behavior. It covers human visual system tasks across prominence, subitizing, prioritizing, free-viewing, and searching.

This Hugging Face release packages the divided 10% test subset described in the paper as parquet shards with embedded image bytes. It contains 8,657 question-answer examples and 7,507 unique raw images referenced through the `image` column. The corresponding paper describes the full HVSBench benchmark with 85,147 multimodal QA pairs across 13 question types and 5 fields.

Paper: **Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior**

Authors: Jiaying Lin, Shuquan Ye, Dan Xu, Wanli Ouyang, Rynson W. H. Lau

Project page: https://jiaying.link/HVSBench/

## Dataset Structure

The dataset is uploaded as parquet shards. The `image` feature contains the image data embedded in parquet, and `real_image_path` preserves the original relative source path under `raw_datasets`.

Columns:

- `image`: raw RGB image embedded in parquet
- `index`: source row index
- `question`: benchmark prompt
- `question_type`: question type ID, Q1-Q13
- `answer`: ground-truth answer
- `answer_type`: answer format, such as `single_choice`, `int_number`, `sorting`, or `fixation_prediction`
- `category`: benchmark category, such as `saliency_rank`, `salient_instance`, or `scanpath`
- `split`: original split label
- `real_image_path`: original relative path under `raw_datasets`
- `origin_dataset`: source dataset name

## Data Statistics

- Rows: 8,657
- Unique images: 7,507
- Categories:
  - `saliency_rank`: 4,431
  - `scanpath`: 3,162
  - `salient_instance`: 1,064
- Original source datasets:
  - `SIFR`: 4,431
  - `COCOFreeView`: 2,519
  - `SIS10K`: 1,064
  - `COCOSearch`: 643

## Usage

```python
from datasets import load_dataset

ds = load_dataset("<repo_id>")
sample = ds["train"][0]
image = sample["image"]
question = sample["question"]
answer = sample["answer"]
```

## Citation

```bibtex
@InProceedings{Lin_HVSBench,
  author    = {Lin, Jiaying and Ye, Shuquan and Xu, Dan and Ouyang, Wanli and Lau, Rynson W.H.},
  title     = {Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior},
  booktitle = {CVPR 2026 Findings},
  year      = {2026},
}
```

## License

The dataset is derived from multiple source datasets (`SIFR`, `COCOFreeView`, `SIS10K`, and `COCOSearch`). Please review and comply with the license and usage terms of the original source datasets when using this release.