Interactive visualisation of collaborative dynamics: a VLM-based approach for behavior and affect analysis
Year: 2026
Type: article
Abstract: ABSTRACT: Collaboration is crucial in design and management, fostering innovation, problem-solving, and decision-making. We explore the use of vision-language models (VLMs) for analyzing collaboration, focusing on detecting social behavior and group affect. By fusing multimodal cues, VLMs enable more context-aware reasoning beyond surface-level perception. We develop a pipeline, a structured prompt and an interactive visualization for integrating VLMs into the analysis workflow. Comparing VLM and human analysis results, we discuss how VLMs can advance collaboration analysis and the remaining challenges.
Authors: Shuyun Liu, Chris McTeague, Hermann Wolfram Klöckner, Susanne Dreyer, Katja Thoring
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W7167017884