Core Viewpoint - 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 images into mathematical representations, enabling AI to "see" and understand visual information [5][17]. - 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 features into a format that the LLM can understand, thus facilitating 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 leverage transformer architecture for visual understanding [7][9]. - The process involves several steps: image patching, flattening and linear projection, adding positional information, and processing through a transformer encoder [9][10][15]. - ViT's ability to encode spatial relationships using rotary position embedding enhances its understanding of image context [13][14]. Group 3: Language Model (LLM) - The LLM processes a combined sequence of visual and textual information, allowing for a richer context in generating responses [20][31]. - It employs a multi-head attention mechanism to capture relationships between visual tokens and textual tokens, enhancing its ability to understand complex queries [19][24]. - The LLM's architecture is evolving towards a mixture of experts (MoE) model, which allows for more efficient processing by activating only a subset of parameters during inference [28][31]. Group 4: Connector Mechanism - The connector's role is crucial in aligning the visual and textual modalities, ensuring that the LLM can effectively interpret the visual features provided by the ViT [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 sophisticated Q-Former, which actively extracts key information from visual features [36][38]. - Q-Former utilizes learnable queries and cross-attention mechanisms to distill essential information from the visual input, reducing the cognitive load on the LLM [42][45]. Group 5: Challenges and Future Directions - The article highlights the challenge of processing high-resolution images without overwhelming the LLM's computational capacity, leading to the exploration of different architectural solutions [54]. - The need for models to efficiently handle complex visual data while maintaining performance is a key focus for future developments in MLLM technology [54].