让视频学习从被动变主动，可点区域提问并获精准回答
Beyond Play and Pause: Turning GPT-4o Spatial Weakness into a Strength for In-Depth Interactive Video Learning
- 用户框选视频区域提问，系统结合视觉与语言理解给出上下文回应
- 利用标注帧替代原始坐标，解决GPT-4o空间定位弱的问题
- 适合教育、内容分析场景，提升学习互动性与理解深度
传统视频学习仍以被动观看为主，缺乏动态交互机会。现有AI工具虽能提供字幕和摘要，但难以实现实时、区域相关的互动。本文提出Untwist系统，支持用户对整个视频或特定区域提问，通过框选边界框获取上下文感知的多模态响应。系统融合GPT API与计算机视觉技术，对视频内容进行提取、处理与结构化，以增强理解。通过使用标注帧而非原始坐标数据，有效克服GPT-4o的空间定位缺陷，显著提升内容定位与解释准确性。论文详述系统架构，包括视频预处理与实时交互机制，展示其将被动视频消费转化为互动式AI学习体验的潜力，有望提升用户参与度与理解力。
原文摘要 · Abstract (English)
Traditional video-based learning remains passive, offering limited opportunities for users to engage dynamically with content. While current AI-powered tools offer transcription and summarization, they lack real-time, region-specific interaction capabilities. This paper introduces Untwist, an AI-driven system that enables interactive video learning by allowing users to ask questions about the entire video or specific regions using a bounding box, receiving context-aware, multimodal responses. By integrating GPT APIs with Computer Vision techniques, Untwist extracts, processes, and structures video content to enhance comprehension. Our approach addresses GPT-4o spatial weakness by leveraging annotated frames instead of raw coordinate data, significantly improving accuracy in localizing and interpreting video content. This paper describes the system architecture, including video pre-processing and real-time interaction, and outlines how Untwist can transform passive video consumption into an interactive, AI-driven learning experience with the potential to enhance engagement and comprehension.
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