Core Insights - The multi-modal large model market in China is projected to reach 15.63 billion yuan in 2024, an increase of 6.54 billion yuan from 2023, and is expected to grow to 23.48 billion yuan in 2025, indicating strong market demand and government support [1][6][19] Multi-Modal Large Model Industry Definition and Classification - Multi-modal large models are AI systems capable of processing and understanding various data forms, including text, images, audio, and video, using deep learning technologies like the Transformer architecture [2][4] Industry Development History - The multi-modal large model industry has evolved through several stages: task-oriented phase, visual-language pre-training phase, and the current multi-modal large model phase, focusing on enhancing cross-modal understanding and generation capabilities [4] Current Industry Status - The multi-modal large model industry has gained significant attention due to its data processing capabilities and diverse applications, with a market size projected to grow substantially in the coming years [6][19] Application Scenarios - The largest application share of multi-modal large models is in the digital human sector at 24%, followed by gaming and advertising at 13% each, and smart marketing and social media at 10% each [8] Industry Value Chain - The industry value chain consists of upstream components like AI chips and hardware, midstream multi-modal large models, and downstream applications across various sectors including education, gaming, and public services [10][12] Competitive Landscape - Major players in the multi-modal large model space include institutions and companies like the Chinese Academy of Sciences, Huawei, Baidu, Tencent, and Alibaba, with various models being developed to optimize training costs and enhance capabilities [16][17] Future Development Trends - The multi-modal large model industry is expected to become more intelligent and humanized, providing richer and more personalized user experiences, with applications expanding across various fields such as finance, education, and content creation [19]

Core Insights - The article discusses the development and comparison of multimodal large models, emphasizing the integration of visual and language components to enhance understanding and generation capabilities in AI systems [1][7]. Multimodal Model Types - The mainstream approach for visual and language multimodal models involves using pre-trained large language models and image encoders, connected through a feature alignment module to enable deeper question-answer reasoning [1]. - CLIP, developed by OpenAI, utilizes a contrastive learning method to connect image and text feature representations, allowing for zero-shot classification by calculating cosine similarity between text and image embeddings [2]. - Flamingo, introduced in 2022, combines visual and language components, enabling text generation based on visual and textual inputs, and includes various datasets for training [5]. - BLIP, proposed by Salesforce in 2022, aims to unify understanding and generation capabilities for visual language tasks, enhancing model performance through self-supervised learning and addressing complex tasks like image generation and visual question answering [7]. - LLaMA integrates a visual encoder (CLIP ViT-L/14) with a language decoder, utilizing generated data for instruction fine-tuning, ensuring that visual and language tokens exist in the same feature space [8].