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
Real-time, accurate, and open source upper-limb musculoskeletal analysis using a single RGBD camera
Amedeo Ceglia, Kael Facon, Mickaël Begon, Lama Seoud
Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions
Haimin Hu, Jonathan DeCastro, Deepak Gopinath, Guy Rosman, Naomi Ehrich Leonard, Jaime Fernández Fisac
Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects
Yizhou Chen, Yiting Zhang, Zachary Brei, Tiancheng Zhang, Yuzhen Chen, Julie Wu, Ram Vasudevan
FlightBench: Benchmarking Learning-based Methods for Ego-vision-based Quadrotors Navigation
Shu-Ang Yu, Chao Yu, Feng Gao, Yi Wu, Yu Wang
Modeling Real-Time Interactive Conversations as Timed Diarized Transcripts
Garrett Tanzer, Gustaf Ahdritz, Luke Melas-Kyriazi
Generative AI and Large Language Models for Cyber Security: All Insights You Need
Mohamed Amine Ferrag, Fatima Alwahedi, Ammar Battah, Bilel Cherif, Abdechakour Mechri, Norbert Tihanyi