从 LLaVA 到 Qwen3-VL：解构多模态大模型的演进之路

Core Insights - The article discusses the evolution of artificial intelligence (AI) from a text-based model to a multimodal large model (MLLM) capable of perceiving and interacting with the physical world through vision and language [1][2]. Group 1: MLLM Architecture - The MLLM architecture is described as a "trinity" system consisting of three core components: the visual encoder (Vision Transformer), the language model (LLM), and the connector [3][5]. - The visual encoder transforms pixel data from images or videos into mathematical representations that the model can understand [5][6]. - The LLM serves as the cognitive center, integrating visual features with textual instructions to generate coherent responses [17][20]. - The connector acts as a bridge, translating visual information into a format that the LLM can process, ensuring seamless communication between the two modalities [32][33]. Group 2: Vision Transformer (ViT) - ViT revolutionizes image processing by treating images as sequences of patches, allowing the model to understand visual data similarly to how it processes text [7][9]. - The process involves several steps: image patching, flattening and linear projection, adding contextual information, and processing through a transformer encoder [10][15]. - ViT's ability to understand spatial relationships is enhanced through techniques like Rotary Position Embedding (RoPE), which encodes positional information dynamically [14][13]. Group 3: Language Model (LLM) - The LLM is responsible for generating responses based on the integrated multimodal input, utilizing mechanisms like multi-head attention and feed-forward networks to process information [19][24]. - The input to the LLM is a combined sequence of visual and textual tokens, allowing for a richer understanding of context [20][22]. - The LLM employs autoregressive generation to predict the next token based on the entire context, iteratively building responses [24][25]. Group 4: Connector Mechanism - The connector's role is crucial in aligning the visual features from ViT with the LLM's understanding, effectively bridging the modality gap [32][34]. - There are two main design philosophies for connectors: the minimalist approach exemplified by LLaVA, which relies on a simple linear transformation, and the more complex Q-Former, which actively extracts key information from visual data [36][38]. - Q-Former utilizes learnable queries and cross-attention mechanisms to distill essential information from the visual input before it reaches the LLM, enhancing efficiency and reducing computational load [42][45]. Group 5: Challenges and Future Directions - The article highlights the challenge of processing high-resolution visual information without overwhelming the LLM's computational capacity, leading to the exploration of different architectural approaches [54].