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
StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation
Akio Kodaira, Chenfeng Xu, Toshiki Hazama, Takanori Yoshimoto, Kohei Ohno, Shogo Mitsuhori, Soichi Sugano, Hanying Cho, Zhijian Liu, Kurt Keutzer
EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping
Weipeng Guan, Peiyu Chen, Huibin Zhao, Yu Wang, Peng Lu
GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis
Yiqing Liang, Numair Khan, Zhengqin Li, Thu Nguyen-Phuoc, Douglas Lanman, James Tompkin, Lei Xiao
Emotion Based Prediction in the Context of Optimized Trajectory Planning for Immersive Learning
Akey Sungheetha, Rajesh Sharma R, Chinnaiyan R