  Moment retrieval (MR) and highlight detection (HD) aim to identify relevant
moments and highlights in video from corresponding natural language query.
Large language models (LLMs) have demonstrated proficiency in various computer
vision tasks. However, existing methods for MR\&HD have not yet been integrated
with LLMs. In this letter, we propose a novel two-stage model that takes the
output of LLMs as the input to the second-stage transformer encoder-decoder.
First, MiniGPT-4 is employed to generate the detailed description of the video
frame and rewrite the query statement, fed into the encoder as new features.
Then, semantic similarity is computed between the generated description and the
rewritten queries. Finally, continuous high-similarity video frames are
converted into span anchors, serving as prior position information for the
decoder. Experiments demonstrate that our approach achieves a state-of-the-art
result, and by using only span anchors and similarity scores as outputs,
positioning accuracy outperforms traditional methods, like Moment-DETR.
