Datasets:

Memories-ai
/

UGC-VideoCap

+---
+license: mit
+task_categories:
+- video-text-to-text
+tags:
+- video-captioning
+- multimodal
+- ugc
+- tiktok
+- audio-visual
+---
+# UGC-VideoCaptioner Dataset
+Real-world user-generated videos, especially on platforms like TikTok, often feature rich and intertwined audio-visual content. However, existing video captioning benchmarks and models remain predominantly visual-centric, overlooking the crucial role of audio in conveying scene dynamics, speaker intent, and narrative context. This lack of full-modality datasets and lightweight, capable models hampers progress in fine-grained, multimodal video understanding. To address these challenges, we introduce **UGC-VideoCap**, a new benchmark and model framework specifically designed for detailed, omnimodal captioning of short-form, user-generated videos. Unlike prior datasets, UGC-VideoCap emphasizes balanced integration of audio and visual modalities, featuring 1,000 TikTok videos annotated through a structured three-stage human-in-the-loop pipeline covering audio-only, visual-only, and joint audio-visual semantics. The benchmark also includes 4,000 carefully crafted QA pairs probing both unimodal and cross-modal understanding.
+## Abstract
+Real-world user-generated videos, especially on platforms like TikTok, often feature rich and intertwined audio visual content. However, existing video captioning benchmarks and models remain predominantly visual centric, overlooking the crucial role of audio in conveying scene dynamics, speaker intent, and narrative context. This lack of omni datasets and lightweight, capable models hampers progress in fine grained, multimodal video understanding. To address these challenges, we introduce UGC-VideoCap, a new benchmark and model framework specifically designed for detailed omnimodal captioning of short form user-generated videos. Unlike prior datasets, UGC-VideoCap emphasizes balanced integration of audio and visual modalities, featuring 1000 TikTok videos annotated through a structured three stage human-in-the-loop pipeline covering audio only, visual only, and joint audio visual semantics. The benchmark also includes 4000 carefully crafted QA pairs probing both unimodal and cross modal understanding. Alongside the dataset, we propose UGC-VideoCaptioner(3B), a 3B parameter captioning model distilled from Gemini 2.5 Flash. Using a novel two-stage training strategy supervised fine tuning followed by Group Relative Policy Optimization (GRPO), our approach enables efficient adaptation from limited data while maintaining competitive performance. Together, our benchmark and model offer a high-quality foundation and a data-efficient solution for advancing omnimodal video captioning in unconstrained real-world UGC settings.
+## Links
+*   **Paper:** [UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks](http://arxiv.org/abs/2507.11336)
+*   **GitHub Repository:** [https://github.com/WPR001/UGC_VideoCaptioner](https://github.com/WPR001/UGC_VideoCaptioner)
+*   **Project Page:** [https://memories.ai/](https://memories.ai/)
+## Benchmark Results
+<p align="center">
+    <img src="https://github.com/WPR001/UGC_VideoCaptioner/blob/main/tiktok_qa_sample.png?raw=true" alt="UGC-VideoCap Sample">
+</p>
+<p align="center">
+    <img src="https://github.com/WPR001/UGC_VideoCaptioner/blob/main/benchmark.png?raw=true" alt="UGC-VideoCap Benchmark Results">
+</p>
+| Model                         | visual | audio | details | average | Link |
+|:------------------------------|:------:|:-----:|:-------:|:-------:|:----:|
+| Gemini-2.5-pro              |    75.8    |   70.8    |    74.8     |     73.78    | N/A  |
+| Gemini-2.5-flash              | **78.8**     |   **74.2**    |    **77.2**    |    **76.73**    | N/A  |
+| Qwen2.5-Omni-3B               |   55.6     |  48.2    |   52.6      |   52.18      | N/A  |
+| UGC-VideoCaptioner-3B-zero(1k RL)         |    57.8    |  53.0     |    55.4     |    55.40(**+3.22**)    | [google-drive](https://drive.google.com/drive/folders/1R-L4kz4R7UxYpcU4El1ctgvVDQbsMsG6?usp=sharing) |
+| Qwen2.5-Omni-3B 1k sft        |    58.4    |   61.4  |   57.0    |     58.96(+6.78)    | [google-drive](https://drive.google.com/drive/folders/1itJ1u4XEJNVfmgbxuKL-fGWCbaW3EAza?usp=sharing) |
+| Qwen2.5-Omni-3B 10k sft       |    58.4   |    63.2   |   58.0     |   59.87(+7.69)     | [google-drive](https://drive.google.com/drive/folders/1auQ4mx9CcxIzAIF4SyH034xufzrqe29w?usp=sharing) |
+| Qwen2.5-Omni-3B 20k sft       |    59.2   |   64    |    58.4   |     60.50(+8.32)     | [google-drive](https://drive.google.com/drive/folders/11WJZkq8I_807zJUmBCCvwNjSj18F2im9?usp=sharing) |
+| UGC-VideoCaptioner-3B (1k SFT + 1k RL)         |   59.4     |    62.4   |    58.2     |    60.01(**+7.83**)   | [google-drive](https://drive.google.com/drive/folders/1LGmIU60cdacErNgUk86D8I5_kiU_ljFz?usp=sharing) |
+## Benchmark Inference (for UGC-VideoCaptioner-3B & Qwen2.5-Omni)
+```bash
+conda create -n test python=3.12
+pip install vllm
+pip install transformers==4.52.3
+```
+```python
+from vllm.assets.video import VideoAsset
+```
+**change the source code in vllm. open the VideoAsset function file and replace the video.py**
+```bash
+cd omni_bench_inference
+CUDA_VISIBLE_DEVICES=0 \
+VLLM_USE_V1=0 \
+python vllm_infer.py
+```
+## Evaluation
+### Final Caption prompt (for inference)
+```python
+prompt = "You are given a short video with both audio and visual content. Write a detailed and coherent paragraph that naturally integrates all modalities. " \
+"Your description should include: (1) the primary scene and background setting; (2) key characters or objects and their actions or interactions; " \
+"(3) significant audio cues such as voices, background music, sound effects, and their emotional tone; " \
+"(4) any on-screen text (OCR) and its role in the video context; and (5) the overall theme or purpose of the video. " \
+"Ensure the output is a fluent and objective paragraph, not a bullet-point list, and captures the video's content in a human-like, narrative style."
+```
+### Score
+**judge by gpt-4o-2024-08-06**
+This version of model was posted before the tikitok video.
+```bash
+python eval_caption.py
+```

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