arXiv Computer Vision
A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data
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HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence
HUG‑VIS is a unified multimodal benchmark for human‑centered visual intelligence, comprising 8,400 half‑body videos of 30 professional actors performing 280 emotion‑action prompts in Mandarin. The dataset provides synchronized video, audio, text, and alpha mattes for four tasks—human emotion recognition, video generation, voice cloning, and video matting—allowing evaluation of both open‑ and closed‑source models under a zero‑shot protocol. Results reveal that linguistic cues dominate emotion recognition, visual affect is weakest, and that automatic metrics and human judgments diverge in generation and cloning tasks, while motion‑related boundary fidelity remains a key challenge for matting.
arXiv AI
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From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes
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arXiv AI
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Mind the Student: Behavioral and Contextual Cues for Automated Engagement Prediction in Online Learning
The paper tackles the challenge of predicting student engagement from online tutoring videos, noting that engagement is a complex, multidimensional construct influenced by behavioral, emotional, and cognitive states. By analyzing the CASED dataset, the authors highlight the difficulty posed by high inter‑person variability and subjective annotations. They propose a multimodal framework that fuses implicit spatiotemporal features from pretrained video, audio, and image encoders with structured behavioral cues such as head pose, gaze, facial action units, emotion, and wavelet‑based audio features, integrating them via a Perceiver IO bottleneck and modeling participant personalities with variational posteriors. The system employs evidential regression and spectral‑normalized Gaussian process classification heads to provide uncertainty‑aware predictions, achieving competitive performance on the CASED challenge test set while offering well‑calibrated uncertainty metrics.
arXiv AI
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RIACT: A Responsible AI System for Personalized Study Habit Tracking and Early Burnout Signal Detection in University Students
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arXiv AI
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From Wearable Data to Personalized and Actionable Health Insights
arXiv:2608. 03251v1 Announce Type: cross Abstract: Commercial wearable devices continuously capture rich physiological data (e.
arXiv AI
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Facial-Expression-Aware Prompting for Empathetic LLM Tutoring
arXiv:2604. 15336v2 Announce Type: replace-cross Abstract: Large language models (LLMs) enable increasingly capable tutoring-style conversational agents, yet effective tutoring requires sensitivity to learners' affective and cognitive states beyond text alone.