Attend to What I Say: Highlighting Relevant Content on Slides

2026-01-15

IEEE International Conference on Document Analysis and Recognition

AI Summary

This work addresses the cognitive overload experienced by audiences during presentations, where simultaneous attention to spoken content and corresponding slide elements often leads to missed key information. To mitigate this, the authors propose a multimodal alignment approach that achieves, for the first time, fine-grained real-time synchronization between the speaker’s utterances and textual, graphical, and layout components of presentation slides. By dynamically highlighting the most relevant slide regions, the method leverages integrated speech recognition, natural language processing, and computer vision techniques to significantly enhance viewers’ comprehension and tracking of fast-paced presentations. The released open-source code and dataset establish a new paradigm for intelligent assistance in educational videos and recorded conference talks.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Imagine sitting in a presentation, trying to follow the speaker while simultaneously scanning the slides for relevant information. While the entire slide is visible, identifying the relevant regions can be challenging. As you focus on one part of the slide, the speaker moves on to a new sentence, leaving you scrambling to catch up visually. This constant back-and-forth creates a disconnect between what is being said and the most important visual elements, making it hard to absorb key details, especially in fast-paced or content-heavy presentations such as conference talks. This requires an understanding of slides, including text, graphics, and layout. We introduce a method that automatically identifies and highlights the most relevant slide regions based on the speaker's narrative. By analyzing spoken content and matching it with textual or graphical elements in the slides, our approach ensures better synchronization between what listeners hear and what they need to attend to. We explore different ways of solving this problem and assess their success and failure cases. Analyzing multimedia documents is emerging as a key requirement for seamless understanding of content-rich videos, such as educational videos and conference talks, by reducing cognitive strain and improving comprehension. Code and dataset are available at: https://github.com/meghamariamkm2002/Slide_Highlight

Problem

• slide understanding
• multimodal alignment
• visual attention
• presentation comprehension
• cognitive load

Innovation

• slide highlighting
• multimodal alignment
• speech-to-visual synchronization
• presentation comprehension
• attention guidance