Intent Lenses: Inferring Capture-Time Intent to Transform Opportunistic Photo Captures into Structured Visual Notes

2026-04-10

AI Summary

This work addresses the challenge of transforming opportunistically captured photographs—such as slides or exhibit displays—into structured notes that accurately reflect user intent. The authors introduce the concept of “Intent Lenses,” which models users’ intentions at the moment of capture as actionable, interactive objects. Leveraging large language models to infer intent, the system generates structured visual notes on a spatial canvas, enabling cross-photo semantic integration and flexible organization. Findings from a user study demonstrate that the resulting notes align closely with users’ expectations, provide effective overviews, and facilitate deeper meaning-making.

Technology Category

• Computer Vision: Language and Vision
• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Opportunistic photo capture (e.g., slides, exhibits, or artifacts) is a common strategy for preserving information encountered in information-rich environments for later revisitation. While fast and minimally disruptive, such photo collections rarely become meaningful notes. Existing automatic note-generation approaches provide some support but often produce generic summaries that fail to reflect what users intended to capture. We introduce Intent Lenses, a conceptual primitive for intent-mediated note generation and sensemaking. Intent Lenses reify users'capture-time intent inferred from captured information into reusable interactive objects that encode the function to perform, the information sources to focus on, and how results are represented at an appropriate level of detail. These lenses are dynamically generated using the reasoning capabilities of large language models. To investigate this concept, we instantiate Intent Lenses in the context of academic conference photos and present an interactive system that infers lenses from presentation captures to generate structured visual notes on a spatial canvas. Users can further add, link, and arrange lenses across captures to support exploration and sensemaking. A study with nine academics showed that intent-mediated notes aligned with users'expectations, providing effective overviews of their captures while facilitating deeper sensemaking.

Problem

• opportunistic photo capture
• capture-time intent
• structured visual notes
• intent inference
• note generation

Innovation

• Intent Lenses
• opportunistic photo capture
• structured visual notes
• large language models
• sensemaking