Fine-Grained Video Indexing and Retrieval with Vision-Language Models

The exponential growth of video content has made it increasingly difficult to locate specific information within large-scale video repositories. To address this challenge, we present a multimodal video indexing framework that integrates audio transcription and visual scene analysis into a unified, searchable structure. The system uses speech-to-text models to transcribe spoken content and applies vision-language models to generate semantic descriptions from video frames. To evaluate the effectiveness of the proposed framework, we conducted experiments on 174 hours of broadcast television content. The results, which include both retrieval accuracy and query latency, confirm the ability of the system to locate relevant sections efficiently and support scalable search in data-intensive environments.