Modality Agnostic Transformer Encoder

Modality-agnostic transformer encoders aim to process diverse data types (images, audio, text, sensor data) using a single, unified architecture, eliminating the need for modality-specific components and improving efficiency. Current research focuses on developing these encoders using transformer-based architectures, often incorporating techniques like set transformers or mixture-of-experts to handle variable input features and improve scalability. This approach promises to significantly advance multimodal learning by enabling more efficient and robust processing of heterogeneous data, leading to improved performance in various applications such as autonomous driving, medical image analysis, and human motion generation.