Multimodal Deep Learning

Multimodal deep learning is a method that integrates information from multiple modalities such as text, images, audio, and video, aiming to enhance the accuracy and comprehensiveness of predictions by synthesizing various types of data. The core challenge lies in effectively fusing information from different modalities, and common techniques include feature fusion and attention mechanisms. Multimodal deep learning is widely applied in areas like image captioning, speech recognition, and autonomous driving, where it can improve the robustness and performance of models, better equipping them to handle complex information in real-world scenarios.