Principle:NVIDIA NeMo Curator Video Captioning
| Knowledge Sources | |
|---|---|
| Domains | Data_Curation, Video_Processing, Multimodal_AI |
| Last Updated | 2026-02-14 17:00 GMT |
Overview
Technique for generating natural language descriptions of video content using vision-language models with optional LLM-based caption refinement.
Description
Video Captioning generates textual descriptions of video clips using a three-stage approach: frame preparation (sampling frames at target FPS and windowing), caption generation (using a vision-language model like Qwen-VL to describe the visual content), and caption enhancement (using a language model to refine and improve the initial captions). This produces rich textual metadata that can be used for video-text alignment, search indexing, and multimodal training.
Usage
Use after frame extraction and filtering. The three stages (preparation, generation, enhancement) should be run in sequence. FP8 quantization is available for reduced memory usage.
Theoretical Basis
- Frame Windowing: Sample frames at target FPS, group into windows of fixed size for batch processing
- Vision-Language Captioning: Feed frame windows to VL model with prompt template to generate descriptions
- Caption Enhancement: Use LLM to improve caption quality, detail, and coherence