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How LLMs analyse images

The technology that allows you to upload images to large language models (LLMs) and have them analyze those images is called Multimodal Large Language Models (Multimodal LLMs). These models are designed to process and interpret multiple types of data inputs, such as text, images, audio, and even video. By integrating both visual and textual data, multimodal LLMs can generate more contextually rich and accurate outputs compared to traditional text-only models.

How Multimodal LLMs Work

Multimodal LLMs combine various machine learning techniques to process different types of inputs. Here’s an overview of the stack of processes involved:

  1. Image Processing:
  2. The first step involves extracting features from the image using a visual encoder. Popular encoders like CLIP or vision transformers (ViTs) are often used for this task. These encoders convert the image into a format that the model can understand by generating visual embeddings.
  3. Text Processing:
  4. Simultaneously, the model processes any accompanying text input using a language model such as GPT or LLaMA. This creates textual embeddings that can be aligned with the visual data.
  5. Cross-Modal Integration:
  6. The core of multimodal models is their ability to merge these visual and textual embeddings. Techniques like cross-attention layers allow the model to combine insights from both modalities, enabling it to relate visual elements to textual descriptions or queries [4][6].
  7. Output Generation:
  8. Once the image and text data are processed, the model generates an output based on its understanding of both inputs. This could be a descriptive caption, an answer to a question about the image, or even more complex tasks like object recognition or scene analysis [6][7].

Common Models for Image Analysis

Several models have been developed specifically for multimodal tasks:

  • LLaVA (Large Language and Vision Assistant): A popular choice for visual tasks, LLaVA combines a vision encoder (like CLIP) with language models (such as LLaMA) to handle image-based queries [1][4].
  • CLIP (Contrastive Language-Image Pretraining): This model is known for its ability to relate images and text by learning joint representations of both modalities [4][6].
  • GPT-4V (GPT-4 with Vision): An extension of GPT-4 that includes vision capabilities, allowing it to process images alongside text [7].

Applications

Multimodal LLMs are used in various fields:

  • Image Captioning: Automatically generating descriptive captions for images.
  • Visual Question Answering (VQA): Answering questions based on image content.
  • Medical Imaging: Assisting in analyzing complex medical imagery like MRIs [4].
  • Object Recognition & Scene Understanding: Identifying objects or interpreting scenes in real-world applications [6].

Technology Stack

To make this technology work efficiently, several components are involved:

  1. Pretrained Models: Multimodal LLMs rely on vast datasets of both text and images during training to learn relationships between these modalities.
  2. Vision Encoders: Models like CLIP or ViTs are used to extract meaningful features from images.
  3. Language Models: Textual data is processed by advanced LLMs like GPT or LLaMA.
  4. Cross-Modality Attention Mechanisms: These mechanisms allow the model to combine visual and textual information effectively.
  5. Hardware Requirements: Running multimodal models locally requires significant computational resources, especially for larger models [3].

In summary, the technology allowing you to upload images to LLMs for analysis is based on multimodal large language models that integrate both visual and textual data through a stack of processes involving feature extraction, cross-modal attention, and advanced machine learning techniques [1][4][6].

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.