Update README.md

README.md

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----
-license: mit
----
+---
+pretty_name: PerceptionComp
+license: other
+task_categories:
+  - video-question-answering
+  - multiple-choice
+language:
+  - en
+tags:
+  - video
+  - benchmark
+  - multimodal
+  - reasoning
+  - video-understanding
+  - evaluation
+size_categories:
+  - 1K<n<10K
+---
+
+# PerceptionComp
+
+PerceptionComp is a benchmark for complex perception-centric video reasoning. It focuses on questions that cannot be solved from a single frame, a short clip, or a shallow caption. Models must revisit visually complex videos, gather evidence across temporally separated segments, and combine multiple perceptual cues before answering.
+
+## Dataset Details
+
+### Dataset Description
+
+PerceptionComp contains 1,114 manually annotated five-choice questions associated with 273 videos. The benchmark covers seven categories: outdoor tour, shopping, sport, variety show, home tour, game, and movie.
+
+This Hugging Face dataset repository hosts the benchmark videos. The official annotation file, evaluation code, and model integration examples are maintained in the GitHub repository:
+
+- GitHub repository: https://github.com/hrinnnn/PerceptionComp
+
+- **Curated by:** PerceptionComp authors
+- **Language(s) (NLP):** English
+- **License:** Please replace `other` in the metadata above with the final data license before public release if a more specific license applies.
+
+### Dataset Sources
+
+- **Repository:** https://github.com/hrinnnn/PerceptionComp
+<!-- - **Paper:** Add the public paper link here when available. -->
+
+## Uses
+
+### Direct Use
+
+PerceptionComp is intended for:
+
+- benchmarking video-language models on complex perception-centric reasoning
+- evaluating long-horizon and multi-evidence video understanding
+- comparing proprietary and open-source multimodal models under a unified protocol
+
+Users are expected to download the videos from this Hugging Face dataset and run evaluation with the official GitHub repository.
+
+### Out-of-Scope Use
+
+PerceptionComp is not intended for:
+
+- unrestricted commercial redistribution of hosted videos when original source terms do not allow it
+- surveillance, identity inference, or sensitive attribute prediction
+- modifying the benchmark protocol and reporting those results as directly comparable official scores
+
+## Dataset Structure
+
+### Data Instances
+
+Each benchmark question is associated with:
+
+- one `video_id`
+- one multiple-choice question
+- five answer options
+- one correct answer
+- one semantic category
+- one difficulty label
+
+The official annotation file is maintained in the GitHub repository:
+
+- `benchmark/annotations/1-1114.json`
+
+Core fields in each annotation item:
+
+- `key`: question identifier
+- `video_id`: video filename stem without `.mp4`
+- `question`: question text
+- `answer_choice_0` to `answer_choice_4`: five answer options
+- `answer_id`: zero-based index of the correct option
+- `answer`: text form of the correct answer
+- `category`: semantic category
+- `difficulty`: difficulty label
+
+### Data Files
+
+The Hugging Face dataset stores the benchmark videos. The official evaluation code prepares them into the following local layout:
+
+```text
+benchmark/videos/<video_id>.mp4
+```
+
+Use the official download script from the GitHub repository:
+
+```bash
+git clone https://github.com/hrinnnn/PerceptionComp.git
+cd PerceptionComp
+pip install -r requirements.txt
+python scripts/download_data.py --repo-id hrinnnn/PerceptionComp
+```
+
+### Data Splits
+
+The current public release uses one official evaluation set:
+
+- `1-1114.json`: 1,114 multiple-choice questions over 273 videos
+
+## Dataset Creation
+
+### Curation Rationale
+
+PerceptionComp was created to evaluate a failure mode that is not well covered by simpler video benchmarks: questions that require models to combine multiple perceptual constraints over time instead of relying on a single salient frame or a short summary.
+
+### Source Data
+
+The benchmark uses real-world videos paired with manually written multiple-choice questions.
+
+#### Data Collection and Processing
+
+Videos were collected and organized for benchmark evaluation. Annotation authors then wrote perception-centric multiple-choice questions for the selected videos. Each question was designed to require visual evidence from the video rather than simple prior knowledge or caption-level shortcuts.
+
+The release process includes:
+
+- associating each question with a `video_id`
+- formatting each sample as a five-choice multiple-choice item
+- assigning semantic categories
+- assigning difficulty labels
+- consolidating the release into one official annotation file
+
+#### Who are the source data producers?
+
+The underlying videos may originate from third-party public sources. The benchmark annotations were created by the PerceptionComp authors and collaborators.
+
+### Annotations
+
+#### Annotation Process
+
+PerceptionComp contains 1,114 manually annotated five-choice questions. Questions were written to test perception-centric reasoning over videos rather than single-frame recognition alone.
+
+#### Who are the annotators?
+
+The annotations were created by the PerceptionComp project team.
+
+#### Personal and Sensitive Information
+
+The videos may contain people, faces, voices, public scenes, or other naturally occurring visual content. The dataset is intended for research evaluation, not for identity inference or sensitive attribute prediction.
+
+### Recommendations
+
+Users should:
+
+- report results with the official evaluation code
+- avoid changing prompts, parsing rules, or metrics when claiming benchmark numbers
+- verify that their usage complies with the terms of the original video sources
+- avoid using the dataset for surveillance, identity recognition, or sensitive attribute inference
+
+## Citation
+
+If you use PerceptionComp, please cite the project paper when it is publicly available.
+
+```bibtex
+@misc{perceptioncomp2026,
+  title={PerceptionComp},
+  author={PerceptionComp Authors},
+  year={2026},
+  howpublished={Hugging Face dataset and GitHub repository}
+}
+```
+
+## More Information
+
+Official evaluation code and documentation:
+
+- GitHub: https://github.com/hrinnnn/PerceptionComp
+
+Example evaluation workflow:
+
+```bash
+git clone https://github.com/hrinnnn/PerceptionComp.git
+cd PerceptionComp
+pip install -r requirements.txt
+python scripts/download_data.py --repo-id hrinnnn/PerceptionComp
+python evaluate/evaluate.py \
+  --model YOUR_MODEL_NAME \
+  --provider api \
+  --api-key YOUR_API_KEY \
+  --base-url YOUR_BASE_URL \
+  --video-dir benchmark/videos
+```
+
+## Dataset Card Authors
+
+PerceptionComp authors
+
+## Dataset Card Contact
+
+Add the project contact email here before publishing.