Person Name
Research on "People" in the context of AI focuses on understanding and improving human-AI interaction across various domains. Current efforts center on enhancing AI's ability to accurately perceive and respond to human nuances, including emotional states, communication styles, and diverse physical characteristics, often employing large language models (LLMs), generative adversarial networks (GANs), and attention networks. This research is crucial for developing more inclusive and effective AI systems, improving accessibility for individuals with disabilities, and mitigating potential biases in AI applications.
Papers
Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks
Marco Matarese, Francesco Rea, Katharina J. Rohlfing, Alessandra Sciutti
Generative Agent Simulations of 1,000 People
Joon Sung Park, Carolyn Q. Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Robb Willer, Percy Liang, Michael S. Bernstein
An Explainable Machine Learning Approach for Age and Gender Estimation in Living Individuals Using Dental Biometrics
Mohsin Ali, Haider Raza, John Q Gan, Ariel Pokhojaev, Matanel Katz, Esra Kosan, Dian Agustin Wahjuningrum, Omnina Saleh, Rachel Sarig, Akhilanada Chaurasia
CameraHMR: Aligning People with Perspective
Priyanka Patel, Michael J. Black