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2025年中国多模态大模型行业主要模型 主要多模态大模型处理能力表现出色【组图】
Qian Zhan Wang·2025-05-22 08:58

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].