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
Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services
Danush Venkateshperumal, Rahman Abdul Rafi, Shakil Ahmed, Ashfaq Khokhar
A Real-Time Defense Against Object Vanishing Adversarial Patch Attacks for Object Detection in Autonomous Vehicles
Jaden Mu
SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live Interactions
Bufang Yang, Yunqi Guo, Lilin Xu, Zhenyu Yan, Hongkai Chen, Guoliang Xing, Xiaofan Jiang
UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time
Lars Schmarje, Kaspar Sakman, Reinhard Koch, Dan Zhang
Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models
Kirill Vasilevski, Dayi Lin, Ahmed Hassan
NeuralDEM - Real-time Simulation of Industrial Particulate Flows
Benedikt Alkin, Tobias Kronlachner, Samuele Papa, Stefan Pirker, Thomas Lichtenegger, Johannes Brandstetter