| --- |
| 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. |
|
|
