Upload HVSBench metadata and raw images

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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+HVSBench_meta_clean_with_image.tsv filter=lfs diff=lfs merge=lfs -text
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HVSBench_meta_clean_with_image.tsv

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README.md

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+---
+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
+---
+
+# 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 cleaned TSV provided with the repository. It contains 8,657 question-answer examples and 7,507 unique raw images. 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 using the Hugging Face `imagefolder` convention. The raw image for each row is exposed as the `image` feature by the dataset viewer. The original relative image path is retained in `real_image_path`, and `HVSBench_meta_clean_with_image.tsv` includes an explicit `image` column pointing to the uploaded raw image file.
+
+Columns:
+
+- `image`: raw RGB image
+- `index`: source row index
+- `real_image_path`: original relative path under `raw_datasets`
+- `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
+- `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
+@misc{lin2026hvsbench,
+  title = {Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior},
+  author = {Lin, Jiaying and Ye, Shuquan and Xu, Dan and Ouyang, Wanli and Lau, Rynson W. H.},
+  year = {2026},
+  note = {Project page: https://jiaying.link/HVSBench/}
+}
+```
+
+## 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.