Multimodal large models are one of the most popular technological directions in the current AI field. Different from traditional single modal models, multimodal models can simultaneously understand and process multiple information types such as text, image, audio, video, etc., and achieve cross modal understanding and generation.
##What is a multimodal model
The core capability of multimodal large models lies in bridging the semantic gap between different data forms. A typical multimodal model can receive an image and a text description, understand the relationship between the two, and use it to complete tasks such as question answering, description generation, or content creation. Mainstream models such as GPT-4V, Gemini, and Claude 3 all possess multimodal capabilities.
##Core Technology Architecture
The current multimodal model mainly adopts the architecture of "encoder fusion decoder". The encoder is responsible for encoding different modalities of input (text, image, audio) into feature vectors separately; The fusion device aligns these feature vectors into a unified semantic space; The decoder generates an output based on the fused information.
In terms of visual encoding, visual language models such as CLIP and SigLIP have established a shared representation space for images and text, enabling the model to understand the correspondence between the textual description of "a cat wearing a hat" and the corresponding image. In terms of audio encoding, models such as Whisper can convert speech signals into text features.
##Comparison of mainstream models
GPT-4V performs well in complex visual reasoning tasks, capable of understanding non-standard visual inputs such as charts, comics, and handwritten notes. The Gemini model natively supports multimodal inputs and places greater emphasis on efficient fusion of cross modal information in its design philosophy. The Claude 3 series also excels in long document comprehension and visual analysis. The domestic Tongyi Qianwen and multimodal versions of ERNIE Bot have the advantage of localization in multimodal understanding of Chinese scenes.
##Application scenarios
The application of multimodal models has covered many fields such as autonomous driving (integrating camera and radar data), intelligent healthcare (combining CT imaging and medical record analysis), content creation (image and text, text and video), intelligent education (recognizing handwritten tasks and providing comments), and is opening a new chapter in AI applications.