State of the Art
Current research focuses on advancing various AI models and algorithms, aiming to improve their performance, efficiency, and applicability across diverse domains. Key areas include enhancing deep learning for time series forecasting and image/video processing, developing more efficient motion planning algorithms for robotics and UAVs, and improving the robustness and interpretability of models for tasks like object detection, scene generation, and medical image analysis. These advancements are significant because they address limitations in existing methods, leading to more accurate, efficient, and reliable AI systems with broad applications in healthcare, manufacturing, robotics, and beyond.
Papers
Multi Teacher Privileged Knowledge Distillation for Multimodal Expression Recognition
Muhammad Haseeb Aslam, Marco Pedersoli, Alessandro Lameiras Koerich, Eric Granger
Comparative Performance Analysis of Transformer-Based Pre-Trained Models for Detecting Keratoconus Disease
Nayeem Ahmed, Md Maruf Rahman, Md Fatin Ishrak, Md Imran Kabir Joy, Md Sanowar Hossain Sabuj, Md. Sadekur Rahman
Metaheuristic Enhanced with Feature-Based Guidance and Diversity Management for Solving the Capacitated Vehicle Routing Problem
Bachtiar Herdianto, Romain Billot, Flavien Lucas, Marc Sevaux
Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection
Xinhao Luo, Man Yao, Yuhong Chou, Bo Xu, Guoqi Li