OmniVideo-100K

---

Official repository for OmniVideo-100K, an instruction-tuning dataset introduced in our paper: "OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains".

This repository includes:

• videos.tar.part_xx: Raw video files.
• train_oe_70k.jsonl: Original Open-Ended (OE) training samples.
• train_mcq_30k.jsonl: Original Multiple-Choice (MCQ) training samples.
• train_oe_70k_formatted.jsonl: Instruction-formatted OE samples (ready for fine-tuning).
• train_mcq_30k_formatted.jsonl: Instruction-formatted MCQ samples (ready for fine-tuning).

Note: The corresponding human-verified test set is released separately as OmniVideo-Test.

---

🔨 Data Generation Pipeline

OmniVideo-100K is generated through an automated two-stage pipeline powered by Gemini series:

1. Entity-Anchored Video Scripting: Transforms raw videos into structured scripts comprising a video summary, a main entity list, and segment-wise audio-visual descriptions with timestamps. The global entity list ensures referential consistency across all segments and associates speech with visual entities.
2. Clue-Guided QA Generation: Instead of processing the entire dense text at once, this step prompts the model to first mine cross-segment, multimodal clues (evidence chains) from the script. QA pairs are subsequently generated based on these high-value clues, guaranteeing long-term temporal spans and deep cross-modal dependencies.

---

🩷 About the Dataset

Dataset Statistics

OmniVideo-100K contains 100K QA pairs derived from 5,214 videos, split into two formats:

• 70K Open-Ended (OE) QA pairs
• 30K Multiple-Choice Questions (MCQ)

Task Taxonomy

We define 10 audio-visual QA tasks organized into a three-level cognitive hierarchy:

• 🔍 Alignment (Basic perception and synchronization)
  • Fine-Grained Perception, Scene Transformation Detection.
• 🧠 Understanding (Cross-modal semantic understanding)
  • Context Understanding, Comparison, Sentiment Analysis, Event Sequence Ordering, Summarization.
• ⚙️ Reasoning (Advanced logical inference)
  • Causal Reasoning, Future Prediction, Hypothetical Reasoning.

Video Curation

Videos are sourced from YouTube. To ensure high-quality data, we apply strict filtering criteria:

• ✅ Resolution ≥ 480p
• ✅ English speech
• ✅ Sufficient visual dynamics & word density (ensuring rich audio-visual information)
• ❌ Hard-coded subtitles removed (using OCR-based tools to prevent models from relying on on-screen text)

Comparison with Existing Datasets

Compared to existing datasets, OmniVideo-100K uniquely provides complex temporal tasks, evidence-based QA, and structured narratives for open-domain videos, enhancing the cross-modal synergistic capabilities of Multimodal Large Language Models.

---

🚀 Performance

To verify the effectiveness of OmniVideo-100K, we performed full-parameter fine-tuning on three open-source MLLMs: VITA-1.5, Qwen2.5-Omni-7B, and Qwen3-Omni-30B-A3B-Instruct.

Performance on OmniVideo-Test

Fine-tuning on OmniVideo-100K leads to substantial performance gains on the OmniVideo-Test benchmark.

Generalization on Existing Benchmarks

Models trained on our dataset exhibit strong generalization transfer, achieving improved performance on audio-visual benchmarks such as Daily-Omni and FutureOmni.

---

📋 Data Format (Raw Data Structure)

We provide both raw .jsonl files and instruction-formatted .jsonl files. Below is the structure for the raw data.

Common Fields

Every sample shares these foundational metadata fields:

{
  "video_id": "...",       // Unique video identifier
  "question_id": "...",    // Unique QA identifier
  "search_tag": "...",     // Retrieval tag (e.g., vlog, news)
  "language": "English",   // Video language
  "duration": 103,         // Video duration in seconds
  "metadata": {},          // Original video metadata
  "video_path": "...",     // Local video path
  "resolution": "1280x720",// Video resolution
  "task": "...",           // Major task category (e.g., causal_reasoning)
  "subtask": "..."         // Subtype (only for Fine-Grained Perception and Context Understanding)
}

General Tasks

Open-Ended:

{
  "question": "Why is the Brunette Girl happy with her blind box?",
  "answer": "She is holding a bottle instead of a can.",
  "analysis": {
    "connections": "...",         // Describes the evidence chains
    "designated_segments": "..."  // Supporting evidence timestamps for cross-segment tasks
  }
}

Multiple-Choice:

{
  "question": "Why is the Brunette Girl happy with her blind box?",
  "options": [
    "She found a rare item.",
    "She is holding a bottle instead of a can.",
    "The blind box was on sale.",
    "She received two figures instead of one."
  ],
  "answer": "B",
  "analysis": {
    "connections": "...",
    "designated_segments": "...",
    "explanation": "..."          // Explains exactly why the correct option is valid
  }
}

Special Task: Event Sequence Ordering

Open-Ended:

{
  "question": "What is the correct chronological sequence for the following events?",
  "events": [                     // Unordered events
    "Event A description...",
    "Event B description...",
    "Event C description..."
  ],
  "answer": ["B", "C", "A"]       // Correct chronological order mapped to the list indices
}

Multiple-Choice:

(To maintain data diversity, the raw data provides both indexed (1) (2) (3) and textual representations of the event sequences.)

{
  "events": [
    "Event A description...",
    "Event B description...",
    "Event C description..."
  ],
  
  // --- Indexed Style ---
  "question_indexed": "What is the correct chronological sequence for the following events?\n(1) Event A\n(2) Event B\n(3) Event C",
  "options_indexed": [
    "(1) → (2) → (3)", 
    "(3) → (2) → (1)", 
    "(2) → (1) → (3)", 
    "(2) → (3) → (1)"
  ],
  
  // --- Textual Style ---
  "question_textual": "What is the correct chronological sequence for the events?",
  "options_textual": [
    "<Event A> → <Event B> → <Event C>", 
    "<Event C> → <Event B> → <Event A>",
    "<Event B> → <Event A> → <Event C>",
    "<Event B> → <Event C> → <Event A>"
  ],
  
  "answer": "D"
}

---

🤖 Instruction-Formatted Data

For direct supervised fine-tuning, we provide _formatted.jsonl files where inputs and outputs are pre-assembled.

• "question": The final constructed prompt fed to the model.
• "answer": The expected target response.

Formatting for General Tasks

For Open-Ended (OE):

// Input
<question>

// Output
<answer>

For Multiple-Choice, we randomly append one of several instruction prompts (either before or after the options):

// Input
<question>
<prompt_prefix>
A. ...
B. ...
C. ...
D. ...
<prompt_suffix>

// Output
<answer> // e.g., "B"

Click to view the randomized MCQ instruction prompts:

We randomly select one pair of [prefix, suffix] from the following list:

1. ["\nSelect from the following choices.", ""]
2. ["\nChoose between the following options.", ""]
3. ["\nAnswer with the option's letter from the given choices directly.", ""]
4. ["", "\nPlease select the correct answer from the options above."]
5. ["", "\nAnswer with the option's letter from the given choices directly."]
6. ["", "\nAnswer with the option's letter directly (e.g., A, B, C, or D)."]
7. ["", "\nAnswer with the option's letter (A, B, C, or D) from the given choices directly."]
8. ["", "\nRespond with only the letter (A, B, C, or D) of the correct option."]

Formatting for Event Sequence Ordering

For Open-Ended:

// Input
<question>
(1) ...
(2) ...
(3) ...
Please directly answer with the correct order of all events' indices, separated by commas.

// Output
2,3,1

For Multiple-Choice:

During formatting, we randomly alternate between the Indexed Style and the Textual Style provided in the raw data to maximize model adaptability.

---

📑 Citation

If you find this dataset or pipeline useful in your research, please cite our paper:

@article{cai2026omnivideo100k,
  title={OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains},
  author={Cai, Xinyue and Fu, Chaoyou and Zhang, Yi-Fan and He, Ran and Shan, Caifeng},
  journal={arXiv preprint arXiv:2606.14702}, 
  year={2026}
}