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
Introducing Delays in Multi-Agent Path Finding
Justin Kottinger, Tzvika Geft, Shaull Almagor, Oren Salzman, Morteza Lahijanian
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, Chunhua Shen
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Mang Ye, Xiuwen Fang, Bo Du, Pong C. Yuen, Dacheng Tao
Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives
Zhongliang Jiang, Septimiu E. Salcudean, Nassir Navab
Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives
Olga Krestinskaya, Li Zhang, Khaled Nabil Salama
A Survey of Spiking Neural Network Accelerator on FPGA
Murat Isik