Multimodal Interaction

Multimodal interaction refers to human-computer interaction using multiple modalities such as text, speech, and images. It aims to integrate the complementary information from different modalities to enhance the system's perception and understanding capabilities. Its core technologies and challenges include:

1.Modal Alignment:

Finding the corresponding relationships or consistency between data from different modalities, ensuring consistency in time and space, thereby enabling more effective fusion and reasoning. For example, aligning actions in a video with speech in audio in the temporal dimension, or aligning pixels in an image with words in text in the spatial dimension.

2.Modal Fusion:

Improving the system's perception accuracy and understanding ability by fusing information from different modalities. For example, in the field of autonomous driving, a multimodal interaction system can simultaneously process traffic rules in text form, image data captured by cameras, and voice instructions, accurately judging road conditions and identifying other vehicles and pedestrians, thereby more safely and effectively planning driving routes.

3.Multimodal Learning:

Utilizing data from multiple different modalities for learning and reasoning, pushing the boundaries of intelligent applications. Through multimodal learning, models can more quickly adapt to new modalities or tasks, improving learning efficiency.

In summary, the AI framework of this product, by integrating multi-agent systems, dialog-based multi-agent collaboration, and multimodal interaction, achieves efficient solutions to complex tasks, tight collaboration between agents, and precise fusion of multimodal information, providing users with a powerful, flexible, and intelligent interaction platform.