Uni Perceiver

Uni-Perceiver is a general-purpose neural network architecture designed to handle diverse perception tasks across various modalities (e.g., vision, language, audio) using a unified model and shared parameters. Current research focuses on improving its efficiency, accuracy, and scalability through techniques like iterative latent attention, early exiting strategies (Dynamic Perceiver), and incorporating Mixture-of-Experts (MoE) for improved task handling. This approach aims to create more efficient and versatile AI systems, reducing the need for task-specific models and potentially impacting fields like robotics, multi-modal understanding, and large-scale data processing.