| --- |
| pretty_name: VidNum-1.4K |
| language: |
| - en |
| - zh |
| license: other |
| task_categories: |
| - question-answering |
| task_ids: |
| - visual-question-answering |
| - multiple-choice-qa |
| size_categories: |
| - 1K<n<10K |
| annotations_creators: |
| - expert-generated |
| source_datasets: |
| - original |
| tags: |
| - video |
| - counting |
| - numerical reasoning |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: VidNum1_4K.jsonl |
| --- |
| |
| # VidNum-1.4K |
|
|
| VidNum-1.4K is a multiple-choice video-based numerical reasoning QA benchmark. |
| Each sample contains a source video link, a timestamp clip range, a question, four choices, and one gold answer. |
|
|
| ## Dataset Summary |
|
|
| - Annotation file: `VidNum1_4K.jsonl` |
| - Number of QA samples: `1379` |
| - Video files found under `videos/`: `1379` mp4 files |
|
|
|
|
| ## Task |
|
|
| - Primary task: video counting question answering |
| - Format: 4-way multiple choice (`A/B/C/D`) |
| - Reasoning labels: `none`, `calculation`, `comparison`, `logic`, `mixed`, `estimation` |
|
|
| ## Data Fields |
|
|
| The JSONL contains the following keys: |
|
|
| - `ID`: integer sample id |
| - `Video Source Link`: source URL of the original video |
| - `Video_path`: corresponding video file. |
| - `Category`: top-level category |
| - `Sub-Category`: fine-grained category |
| - `Timestamp`: clip range in `mm:ss-mm:ss` |
| - `question`: English question text |
| - `option_A`: option A |
| - `option_B`: option B |
| - `option_C`: option C |
| - `option_D`: option D |
| - `Answer`: correct option in `{A, B, C, D}` |
| - `Level`: difficulty level in `{1, 2, 3}` |
| - `Reasoning_Type`: reasoning tag |
| - `Count_Scope`: counting scope tag |
|
|
|
|
