@edwardbae0060607

A comprehensive prototype pipeline for local video captioning, designed to operate efficiently within strict time and memory constraints. The project primarily delivers a highly optimized, public Docker image that serves as a hosted API, capable of taking native video inputs and generating rich, multi-style captions in a single batched process to minimize latency and eliminate redundant computation. At its core, the system utilizes a multi-stage multimodal architecture. It first analyzes the source video to establish a grounded foundational summary. This core understanding is then used to dynamically generate captions tailored to various requested styles. To ensure high reliability, the pipeline includes a robust fallback mechanism that automatically switches to analyzing downscaled sampled frames if the primary native video analysis encounters any issues.

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