UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New
  Benchmarks
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.
6877a06dff8f47a7f8644304
Peiran Wu
claimed_verified
2025-07-16T15:05:41.669Z
6667047b8fdab121b5a32306
/avatars/35fce0470a67ea07bd99a28196ee2cd0.svg
peiran wu
user
peiranW
6877a06dff8f47a7f8644305
Yunze Liu
6877a06dff8f47a7f8644306
Zhengdong Zhu
6877a06dff8f47a7f8644307
Enmin Zhou
6877a06dff8f47a7f8644308
Shawn Shen
UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New
  Benchmarks
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.
UGC-VideoCap introduces a new benchmark and model for detailed omnimodal captioning of short-form user-generated videos, emphasizing balanced audio-visual integration and using a novel two-stage training strategy.
UGC-VideoCap UGC-VideoCaptioner(3B) Gemini 2.5 Flash Group Relative Policy Optimization (GRPO) multimodal captioning human-in-the-loop pipeline audio-visual semantics QA pairs supervised fine tuning