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
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
GSTalker: Real-time Audio-Driven Talking Face Generation via Deformable Gaussian Splatting
Bo Chen, Shoukang Hu, Qi Chen, Chenpeng Du, Ran Yi, Yanmin Qian, Xie Chen
Mesh-based Photorealistic and Real-time 3D Mapping for Robust Visual Perception of Autonomous Underwater Vehicle
Jungwoo Lee, Younggun Cho
Hardware Accelerators for Autonomous Cars: A Review
Ruba Islayem, Fatima Alhosani, Raghad Hashem, Afra Alzaabi, Mahmoud Meribout
CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
Anubhav Bhatti, Prithila Angkan, Behnam Behinaein, Zunayed Mahmud, Dirk Rodenburg, Heather Braund, P. James Mclellan, Aaron Ruberto, Geoffery Harrison, Daryl Wilson, Adam Szulewski, Dan Howes, Ali Etemad, Paul Hungler