Real Time
Real-time processing focuses on developing systems capable of analyzing and responding to data instantaneously, crucial for applications demanding immediate feedback. Current research emphasizes efficient algorithms and model architectures, such as those based on deep learning, to reduce computational latency in diverse domains including robotics, healthcare, and AI-assisted tutoring. This field's advancements are driving progress in areas like autonomous navigation, personalized healthcare monitoring, and human-computer interaction, enabling more responsive and effective systems.
Papers
STITCHER: Real-Time Trajectory Planning with Motion Primitive Search
Helene J. Levy, Brett T. Lopez
Vinci: A Real-time Embodied Smart Assistant based on Egocentric Vision-Language Model
Yifei Huang, Jilan Xu, Baoqi Pei, Yuping He, Guo Chen, Lijin Yang, Xinyuan Chen, Yaohui Wang, Zheng Nie, Jinyao Liu, Guoshun Fan, Dechen Lin, Fang Fang, Kunpeng Li, Chang Yuan, Yali Wang, Yu Qiao, Limin Wang
Real-time One-Step Diffusion-based Expressive Portrait Videos Generation
Hanzhong Guo, Hongwei Yi, Daquan Zhou, Alexander William Bergman, Michael Lingelbach, Yizhou Yu
Deploying Foundation Model Powered Agent Services: A Survey
Wenchao Xu, Jinyu Chen, Peirong Zheng, Xiaoquan Yi, Tianyi Tian, Wenhui Zhu, Quan Wan, Haozhao Wang, Yunfeng Fan, Qinliang Su, Xuemin Shen
Large-scale Group Brainstorming using Conversational Swarm Intelligence (CSI) versus Traditional Chat
Louis Rosenberg, Hans Schumann, Christopher Dishop, Gregg Willcox, Anita Woolley, Ganesh Mani
Thermodynamics-informed graph neural networks for real-time simulation of digital human twins
Lucas Tesán, David González, Pedro Martins, Elías Cueto