Despite progress in video-language modeling, the computational challenge of interpreting long-form videos in response to task-specific linguistic queries persists, largely due to the complexity of high-dimensional video data and the misalignment between language and visual cues over space and time. To tackle this issue, we introduce a novel approach called Language-guided Spatial-Temporal Prompt Learning (LSTP). This approach features two key components: a Temporal Prompt Sampler (TPS) with optical flow prior that leverages temporal information to efficiently extract relevant video content, and a Spatial Prompt Solver (SPS) that adeptly captures the intricate spatial relationships between visual and textual elements. By harmonizing TPS and SPS with a cohesive training strategy, our framework significantly enhances computational efficiency, temporal understanding, and spatial-temporal alignment. Empirical evaluations across two challenging tasks--video question answering and temporal question grounding in videos--using a variety of video-language pretrainings (VLPs) and large language models (LLMs) demonstrate the superior performance, speed, and versatility of our proposed LSTP paradigm.

通过引入一种名为语言导向的时空提示学习（LSTP）的新方法，利用时间信息高效提取相关视频内容，并巧妙捕捉视觉和文本元素之间的复杂空间关系，从而显著提高计算效率、时间理解和时空对齐，通过两个挑战性任务的实证评估，证明了我们提出的LSTP范型在视频问答和视频中的时间问题定位方面具有卓越的性能、速度和多功能性。

LSTP：语言引导的时空提示学习长文 视频文本理解

LSTP：语言引导的时空提示学习长文视频文本理解