EgoInstruct: An Egocentric Video Dataset of Face-to-face Instructional Interactions with Multi-modal LLM Benchmarking

• URL: http://arxiv.org/abs/2509.22019v1
• Date: Fri, 26 Sep 2025 07:52:26 GMT
• Title: EgoInstruct: An Egocentric Video Dataset of Face-to-face Instructional Interactions with Multi-modal LLM Benchmarking
• Authors: Yuki Sakai, Ryosuke Furuta, Juichun Yen, Yoichi Sato,
• Abstract summary: We present a new egocentric video dataset of face-to-face instruction.<n>We provide ground-truth annotations for two fundamental tasks that serve as a first step toward a comprehensive understanding of instructional interactions.<n>We evaluate recently introduced MLLMs that jointly process images, audio, and text.
• Score: 20.1877648578032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analyzing instructional interactions between an instructor and a learner who are co-present in the same physical space is a critical problem for educational support and skill transfer. Yet such face-to-face instructional scenes have not been systematically studied in computer vision. We identify two key reasons: i) the lack of suitable datasets and ii) limited analytical techniques. To address this gap, we present a new egocentric video dataset of face-to-face instruction and provide ground-truth annotations for two fundamental tasks that serve as a first step toward a comprehensive understanding of instructional interactions: procedural step segmentation and conversation-state classification. Using this dataset, we benchmark multimodal large language models (MLLMs) against conventional task-specific models. Since face-to-face instruction involves multiple modalities (speech content and prosody, gaze and body motion, and visual context), effective understanding requires methods that handle verbal and nonverbal communication in an integrated manner. Accordingly, we evaluate recently introduced MLLMs that jointly process images, audio, and text. This evaluation quantifies the extent to which current machine learning models understand face-to-face instructional scenes. In experiments, MLLMs outperform specialized baselines even without task-specific fine-tuning, suggesting their promise for holistic understanding of instructional interactions.

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