| --- |
| license: mit |
| task_categories: |
| - question-answering |
| - visual-question-answering |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - video |
| - hallucination |
| - benchmark |
| - video-language-models |
| configs: |
| - config_name: external_factual |
| data_files: |
| - split: test |
| path: external_factual/external_factual.json |
| - config_name: external_nonfactual |
| data_files: |
| - split: test |
| path: external_nonfactual/external_nonfactual.json |
| - config_name: fact_detect |
| data_files: |
| - split: test |
| path: |
| - fact_detect/fact_detect.json |
| - fact_detect/fact_detect_yn.json |
| - config_name: object_relation |
| data_files: |
| - split: test |
| path: object_relation/object_relation.json |
| - config_name: semantic_detail |
| data_files: |
| - split: test |
| path: semantic_detail/semantic_detail.json |
| - config_name: temporal |
| data_files: |
| - split: test |
| path: temporal/temporal.json |
| --- |
| |
| # VideoHallucer (mirror) |
|
|
| A redistribution of the **VideoHallucer** benchmark, bundled together with its |
| videos so the whole benchmark comes down in a single `snapshot_download`. |
|
|
| > This is **not** the official release. All credit goes to the original authors. |
| > Official code: https://github.com/patrick-tssn/VideoHallucer · |
| > Official data: https://huggingface.co/datasets/bigai-nlco/VideoHallucer |
|
|
| ## Dataset description |
|
|
| - **Repository:** [VideoHallucer](https://github.com/patrick-tssn/VideoHallucer) |
| - **Paper:** [2406.16338](https://arxiv.org/abs/2406.16338) |
| - **Leaderboard:** https://videohallucer.github.io/ |
| - **Point of contact:** [Yuxuan Wang](mailto:wangyuxuan1@bigai.ai) |
|
|
| VideoHallucer is the first comprehensive benchmark for hallucination detection |
| in large video-language models (LVLMs). It categorizes hallucinations into |
| **intrinsic** and **extrinsic** types, with subcategories for object-relation, |
| temporal, semantic detail, external factual, and external non-factual |
| hallucination. |
|
|
| Evaluation is **adversarial binary VideoQA**: every item is a *pair* of yes/no |
| questions over the same video — a `basic` question whose answer is grounded in |
| the video, and a `hallucination` question crafted so a hallucinating model |
| answers it wrongly. A model is only credited when it gets **both** right, which |
| defeats the trivial always-yes / always-no strategies that binary QA otherwise |
| rewards. |
|
|
| ## Data statistics |
|
|
| | | Object-Relation | Temporal | Semantic Detail | External Factual | External Non-factual | |
| | ---- | ---- | ---- | ---- | ---- | ---- | |
| | Questions | 400 | 400 | 400 | 400 | 400 | |
| | Videos | 183 | 165 | 400 | 200 | 200 | |
|
|
| ## Contents |
|
|
| ``` |
| object_relation/ object_relation.json + videos/ |
| temporal/ temporal.json + videos/ |
| semantic_detail/ semantic_detail.json + videos/ |
| external_factual/ external_factual.json + videos/ |
| external_nonfactual/ external_nonfactual.json + videos/ |
| fact_detect/ fact_detect.json, fact_detect_yn.json, modify.py + videos/ |
| interaction/ interaction.json + videos/ |
| ``` |
|
|
| Each subset directory carries its own `videos/` folder, and the `video` field in |
| each JSON is a bare filename resolved against that sibling folder. The |
| `external_factual` and `external_nonfactual` subsets are posed over the same |
| video pool, so their `videos/` folders overlap. |
|
|
| `interaction/` is an extra subset present in the upstream data drop; it is not |
| part of the five headline categories above and is not exposed as a `datasets` |
| config. |
|
|
| ### Record format |
|
|
| Every record pairs two questions over one video: |
|
|
| ```json |
| { |
| "basic": { |
| "video": "1052_6143391925_916_970.mp4", |
| "question": "Is there a baby in the video?", |
| "answer": "yes" |
| }, |
| "hallucination": { |
| "video": "1052_6143391925_916_970.mp4", |
| "question": "Is there a doll in the video?", |
| "answer": "no" |
| }, |
| "type": "subject" |
| } |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| root = snapshot_download(repo_id="shuzhig/VideoHallucer", repo_type="dataset") |
| ``` |
|
|
| A single subset: |
|
|
| ```python |
| snapshot_download( |
| repo_id="shuzhig/VideoHallucer", |
| repo_type="dataset", |
| allow_patterns="temporal/*", |
| ) |
| ``` |
|
|
| Annotations only, no videos: |
|
|
| ```python |
| snapshot_download( |
| repo_id="shuzhig/VideoHallucer", |
| repo_type="dataset", |
| allow_patterns="*/*.json", |
| ) |
| ``` |
|
|
| ## Evaluation |
|
|
| Use the official |
| [VideoHallucerKit](https://github.com/patrick-tssn/VideoHallucer?tab=readme-ov-file#videohallucerkit). |
| The directory layout here matches what the kit expects, so pointing it at the |
| downloaded snapshot root is enough. |
|
|
| ## Provenance and licensing |
|
|
| Mirrored from |
| [`bigai-nlco/VideoHallucer`](https://huggingface.co/datasets/bigai-nlco/VideoHallucer), |
| released under the **MIT license**. Source videos are drawn from existing public |
| video corpora and remain subject to their original terms. This mirror asserts no |
| additional rights. If you are an author and would like it removed, please open a |
| discussion. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2024videohallucer, |
| title = {VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models}, |
| author = {Wang, Yuxuan and Wang, Yueqian and Zhao, Dongyan and Xie, Cihang and Zheng, Zilong}, |
| journal = {arXiv preprint arXiv:2406.16338}, |
| year = {2024} |
| } |
| ``` |
|
|
