Multimodal Language-Image Model (MLIM)

A Multimodal Language-Image Model (MLIM) is an AI model that can accept and understand both text data and image data.

• Context:
  • It can (typically) be trained on large datasets comprising both textual and visual data.
  • It can (typically) handle tasks that require understanding of the relationships between textual descriptions and visual content.
  • It can be employed in diverse applications ranging from image retrieval to image generation based on textual descriptions.
  • It can (typically) be a part of larger systems, such as recommendation systems, where both text and images play crucial roles.
  • It can leverage transfer learning by using pre-trained models on both text and image datasets to improve performance on specific tasks.
  • It can be enhanced with attention mechanisms to focus on relevant parts of the image or text depending on the input or the task at hand.
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• Example(s):
  • Generating Images with Multimodal Language Models: This model fuses frozen text-only large language models (LLMs) with pre-trained image encoder and decoder models, by mapping between their embedding spaces. It can process arbitrarily interleaved image and text inputs to generate coherent image (and text) outputs.
  • Language-Image MoE2: This model is a sparse mixture of experts model capable of multimodal learning. It accepts both images and text simultaneously, while being trained using a contrastive loss. It can learn an appropriate partitioning of modalities using expert layers.
  • LLaMA Model: Introduced by Liu, Li et al., this model is trained on machine-generated instruction-following data which improves its zero-shot capabilities. It's an end-to-end trained large multimodal model that connects a vision encoder with an LLM for general-purpose visual and language understanding.
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• Counter-Example(s):
  • A text-only Natural Language Processing (NLP) model which can't understand or process images.
  • An image-only Convolutional Neural Network (CNN) which is specialized for image classification and does not handle text data.
  • A Unimodal model that is designed to process only one type of data (either text or image).
• See: Multimodal Learning, Transfer Learning, Attention Mechanism, Image Captioning, Visual Question Answering.

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References

2023

• (Liu, Li et al., 2023) ⇒ Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. (2023). “Visual Instruction Tuning.” In: arXiv preprint arXiv:2304.08485. doi:10.48550/arXiv.2304.08485
  • ABSTRACT: Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images / instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.

2023

• GBard
  • A multimodal language-image model (MLIM) is a type of artificial intelligence (AI) model that can process and understand both text and images. MLIMs are trained on large datasets of text and images, which allows them to learn the relationships between the two modalities. This enables MLIMs to perform a variety of tasks, such as:
    • Image retrieval: MLIMs can be used to retrieve images that are relevant to a given text query. For example, an MLIM could be used to retrieve images of cats if the user enters the text query "cat."
    • Image captioning: MLIMs can be used to generate captions for images. This can be useful for people with visual impairments, or for creating more engaging social media posts.
    • Visual question answering: MLIMs can be used to answer questions about images. For example, an MLIM could answer the question "What is the breed of this dog?" if given an image of a dog.
    • Image generation: MLIMs can be used to generate new images from text descriptions. This can be used to create realistic images for movies and video games, or to create new marketing materials.

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