---
language:
- en
pretty_name: VideoMemory-Bench
tags:
- video
- multimodal
- video-question-answering
- long-video-understanding
- benchmark
- multiple-choice
task_categories:
- visual-question-answering
license: other
size_categories:
- 1K<n<10K
---

# VideoMemory-Bench

VideoMemory-Bench is a multiple-choice video question answering benchmark designed to evaluate video memory, long-range temporal understanding, cross-modal association, and reasoning over video content. The released benchmark split contains **5,360 QA pairs** over **3,587 unique videos**. Each QA item is attached to one video, one normalized level, one normalized category code, and one answer label.

This dataset card describes the Hugging Face release format. The public release renames videos to stable anonymous file names such as `vmb_000001.mp4` and renumbers QA pairs in a VideoMME-style format such as `000001-001`.

## Dataset Summary

- **Task:** multiple-choice video question answering.
- **Modality:** video + text question + text options.
- **Split:** `test`.
- **Number of QA pairs:** 5,360.
- **Number of unique videos:** 3,587.
- **Total unique-video duration:** 418.67 hours.
- **Question language:** English.
- **Answer format:** one option letter, e.g. `A`, `B`, `C`, `D`, or `E`.
- **Option count:** variable; each question has 2 to 5 options.
- **Video naming:** public videos are renamed as `videos/vmb_000001.mp4`, `videos/vmb_000002.mp4`, etc.
- **QA naming:** public QA IDs use `{video_id}-{question_index}`, e.g. `000001-001`.

## Repository Structure

```text
VideoMemory-Bench/
  README.md
  data/
    test.jsonl
  videos/
    vmb_000001.mp4
    vmb_000002.mp4
    ...
  metadata/
    video_map.jsonl
    category_schema.json
    checksums.sha256
```

### Main Annotation File

`data/test.jsonl` contains one JSON object per QA pair.

```json
{
  "video_id": "000001",
  "video": "videos/vmb_000001.mp4",
  "duration": "medium",
  "duration_sec": 516.27,
  "source_dataset": "LongVideoBench",
  "original_video_id": "lvb_8hhcFRoR0mw",
  "question_id": "000001-001",
  "original_question_id": "lvb_15",
  "level": "L2",
  "level_name": "Motion & Cross-Modal Association",
  "category_code": "2.1",
  "task_type": "Action & State Recognition",
  "question": "On a brown floor, a woman with black hair, wearing a white top and a green skirt, is kneeling on a white mat. On the leftmost side of the white table in front of her, there's a metal pot. What is this woman doing?",
  "options": [
    "A. Making dumplings",
    "B. Watching TV",
    "C. Listening to music",
    "D. Drinking water",
    "E. Stir-frying vegetables"
  ],
  "answer": "A"
}
```

### Field Definitions

| Field | Type | Description |
|---|---:|---|
| `video_id` | string | Public six-digit video ID assigned by first appearance in the release annotation file. |
| `video` | string | Relative path to the renamed video file in this repository. |
| `duration` | string | Duration bucket: `short`, `medium`, or `long`. |
| `duration_sec` | float | Video duration in seconds. |
| `source_dataset` | string | Source collection name before release normalization. |
| `original_video_id` | string | Original internal video identifier retained for traceability. |
| `question_id` | string | Public QA ID in `{video_id}-{index}` format. |
| `original_question_id` | string | Original internal QA identifier retained for traceability. |
| `level` | string | Normalized benchmark level: `L1` to `L5`. |
| `level_name` | string | Human-readable level name. |
| `category_code` | string | Normalized numeric category code. |
| `task_type` | string | Human-readable category name. |
| `question` | string | Multiple-choice question. |
| `options` | list[string] | Candidate answers formatted as `A. ...`, `B. ...`, etc. |
| `answer` | string | Correct option letter. |

`metadata/video_map.jsonl` maps each public video ID to its release filename and original identifier. Local absolute paths used during preprocessing are not part of the public release.

`metadata/category_schema.json` stores the normalized level and category taxonomy used by `data/test.jsonl`.

## Duration Buckets

Duration buckets follow the same broad short/medium/long convention used in long-video evaluation:

| Bucket | Rule |
|---|---:|
| `short` | `duration_sec < 180` |
| `medium` | `180 <= duration_sec < 900` |
| `long` | `duration_sec >= 900` |

## Data Statistics

### Overall

| Metric | Value |
|---|---:|
| QA pairs | 5,360 |
| Unique videos | 3,587 |
| Total unique-video duration | 418.67 hours |
| Minimum video duration | 1.25 seconds |
| Median video duration | 26.01 seconds |
| Average video duration | 420.19 seconds |
| Maximum video duration | 7,331.29 seconds |

### Source Distribution

| Source dataset | QA pairs | Unique videos |
|---|---:|---:|
| LongVideoBench | 203 | 101 |
| MVBench | 2,731 | 2,539 |
| VSI-Super-Count | 358 | 37 |
| VSI-Super-Recall | 12 | 12 |
| Video-MME | 1,983 | 880 |
| VideoMem-Bench | 73 | 18 |

### Duration Distribution

| Duration bucket | QA pairs | Unique videos |
|---|---:|---:|
| short | 3,176 | 2,830 |
| medium | 467 | 265 |
| long | 1,717 | 492 |

### Level Distribution

| Level | Level name | QA pairs |
|---|---|---:|
| L1 | Basic Visual Perception | 393 |
| L2 | Motion & Cross-Modal Association | 393 |
| L3 | Long-range Video Memory | 3,306 |
| L4 | Reasoning & Commonsense | 1,186 |
| L5 | Robustness & Negative Memory | 82 |

### Option and Answer Distribution

| Number of options | QA pairs |
|---:|---:|
| 2 | 64 |
| 3 | 896 |
| 4 | 4,303 |
| 5 | 97 |

| Answer label | QA pairs |
|---|---:|
| A | 1,620 |
| B | 1,670 |
| C | 1,365 |
| D | 685 |
| E | 20 |

## Category Taxonomy

All public records use normalized numeric category codes. The original working files contained mixed category labels such as `L3`, `L4`, `l3_counting`, and `CROSS`; these are normalized before release.

| Level | Level name | Category code | Task type | QA pairs |
|---|---|---:|---|---:|
| L1 | Basic Visual Perception | 1.1 | Object & Attribute Recognition | 293 |
| L1 | Basic Visual Perception | 1.2 | Video OCR / Text Spotting | 100 |
| L2 | Motion & Cross-Modal Association | 2.1 | Action & State Recognition | 309 |
| L2 | Motion & Cross-Modal Association | 2.2 | Speed & Trajectory | 81 |
| L2 | Motion & Cross-Modal Association | 2.3 | Cross-Modal Associative Memory | 3 |
| L3 | Long-range Video Memory | 3.1.1 | Counting | 571 |
| L3 | Long-range Video Memory | 3.1.2 | Entity State Evolution / State Tracking | 550 |
| L3 | Long-range Video Memory | 3.2.1 | Scene & Synopsis | 784 |
| L3 | Long-range Video Memory | 3.2.2 | Visual Needle-in-a-Haystack (NIAH) | 123 |
| L3 | Long-range Video Memory | 3.3.1 | Action Sequencing & Temporal Localization | 770 |
| L3 | Long-range Video Memory | 3.3.2 | Duration Estimation | 128 |
| L3 | Long-range Video Memory | 3.4.1 | Dynamic Spatial Relationships | 82 |
| L3 | Long-range Video Memory | 3.4.2 | Egocentric Navigation | 225 |
| L3 | Long-range Video Memory | 3.4.3 | 3D Layout Inference | 73 |
| L4 | Reasoning & Commonsense | 4.1.1 | Explanatory / Why-QA | 238 |
| L4 | Reasoning & Commonsense | 4.1.2 | Physical Commonsense | 85 |
| L4 | Reasoning & Commonsense | 4.1.3 | Feasibility | 67 |
| L4 | Reasoning & Commonsense | 4.2.1 | Intent & Belief Tracking (Theory of Mind) | 119 |
| L4 | Reasoning & Commonsense | 4.2.2 | Social Norms & Script Inference | 69 |
| L4 | Reasoning & Commonsense | 4.2.3 | Pragmatic Inference & Deception Detection | 45 |
| L4 | Reasoning & Commonsense | 4.3.1 | Counterfactual | 236 |
| L4 | Reasoning & Commonsense | 4.3.2 | Hypothetical Scenarios | 55 |
| L4 | Reasoning & Commonsense | 4.3.3 | Anticipation / Prediction | 272 |
| L5 | Robustness & Negative Memory | 5.1 | Negative Memory & Hallucination Detection | 82 |

## Evaluation Protocol

Models should answer each question with exactly one option letter. The default metric is exact-match accuracy over normalized answer labels.

Recommended prompt format:

```text
Question: {question}
Options:
{options}
Answer with the option letter only.
```

Prediction normalization should strip whitespace and punctuation, then take the first valid label among `A`, `B`, `C`, `D`, and `E`. A prediction is correct when the normalized label equals `answer`.

## Loading the Dataset

Using `datasets`:

```python
from datasets import load_dataset

dataset = load_dataset("YOUR_ORG/VideoMemory-Bench", split="test")
print(dataset[0])
```

Using plain Python:

```python
import json
from pathlib import Path

rows = [
    json.loads(line)
    for line in Path("data/test.jsonl").read_text().splitlines()
]
```

Each row contains a relative video path. Join it with the dataset repository root to locate the corresponding video file.

## Preprocessing and Normalization

The release uses the 5,360-QA benchmark version. During release packaging:

1. Videos are renamed to `vmb_000001.mp4`, `vmb_000002.mp4`, etc.
2. QA pairs are renumbered by public video ID and within-video question index.
3. Mixed category codes are normalized to numeric category codes.
4. The normalized `level` and `level_name` fields are derived from `category_code`.
5. Options are formatted consistently as `A. ...`, `B. ...`, etc.
6. Answers are normalized to single option letters.
7. Local machine paths are removed from public annotation files.

The source annotation file for this packaged split has SHA-256:

```text
ee0b4d6d4dcbac8ef0a3c7df4286657983c2eb42c415fe6b7ef9329a3eea1217
```

## Intended Use

VideoMemory-Bench is intended for research on video-language models, long-video understanding, video memory, temporal grounding, multimodal reasoning, and robustness to negative or hallucinated memory claims.

Appropriate uses include:

- Evaluating multiple-choice video QA accuracy.
- Comparing models across short, medium, and long videos.
- Analyzing performance by level and task category.
- Studying failures in long-range temporal reasoning and memory retrieval.

## Limitations

- The benchmark is multiple-choice; it does not measure open-ended generation quality directly.
- Option counts vary from 2 to 5, so evaluation code should not assume exactly four options.
- Some videos originate from existing public video QA resources; users should check the upstream dataset terms before redistribution or commercial use.
- The benchmark can contain visual scenes involving people, public online videos, subtitles, and other real-world content.
- Answer distributions are not perfectly balanced, especially for option `E`.
- Public video renaming improves release consistency but does not anonymize the semantic content of the videos.

## License and Terms

**TODO before public release:** specify the final dataset license and any required upstream attribution or usage constraints.

Because the benchmark aggregates or derives examples from multiple source collections, downstream users are responsible for following the applicable terms for the videos and annotations.

## Citation

If you use VideoMemory-Bench, please cite the dataset paper or technical report.

```bibtex
@misc{videomemorybench,
  title        = {VideoMemory-Bench},
  author       = {TODO},
  year         = {2026},
  howpublished = {Hugging Face dataset},
  url          = {TODO}
}
```

## Contact

For questions about the benchmark, data packaging, or evaluation protocol, please contact:

```text
TODO: maintainer name / email / project page
```