Multimodal Perception

Multimodal perception research aims to create systems that integrate information from multiple sensory modalities (e.g., vision, audio, touch) for improved understanding and interaction with the environment. Current research focuses on developing unified model architectures, often based on transformers and incorporating techniques like attention mechanisms and mixture-of-experts, to efficiently process and fuse diverse data streams for tasks such as object detection, segmentation, and robot control. This field is crucial for advancing artificial intelligence, particularly in robotics and autonomous systems, by enabling more robust, adaptable, and human-like perception capabilities in complex real-world scenarios.