Temporal Sentence Grounding (TSG), which aims to localize moments from videos
based on the given natural language queries, has attracted widespread
attention. Existing works are mainly designed for short videos, failing to
handle TSG in long videos, which poses two challenges: i) complicated contexts
in long videos require temporal reasoning over longer moment sequences, and ii)
multiple modalities including textual speech with rich information require
special designs for content understanding in long videos. To tackle these
challenges, in this work we propose a Grounding-Prompter method, which is
capable of conducting TSG in long videos through prompting LLM with multimodal
information. In detail, we first transform the TSG task and its multimodal
inputs including speech and visual, into compressed task textualization.
Furthermore, to enhance temporal reasoning under complicated contexts, a
Boundary-Perceptive Prompting strategy is proposed, which contains three folds:
i) we design a novel Multiscale Denoising Chain-of-Thought (CoT) to combine
global and local semantics with noise filtering step by step, ii) we set up
validity principles capable of constraining LLM to generate reasonable
predictions following specific formats, and iii) we introduce one-shot
In-Context-Learning (ICL) to boost reasoning through imitation, enhancing LLM
in TSG task understanding. Experiments demonstrate the state-of-the-art
performance of our Grounding-Prompter method, revealing the benefits of
prompting LLM with multimodal information for TSG in long videos.

通过使用多模态信息，我们提出了一种用于处理长视频中的 Temporal Sentence Grounding 任务的 Grounding-Prompter 方法，通过引导 LLM 进行 TSG，提高了推理能力和理解 TSG 任务的性能。