Empowering AI with Senses: A Journey into Multimodal LLMs Part 1

Multimodal Large Language Models (LLMs): Bridging the Gap Between Text and Vision

Our world is experienced through multiple senses – language, sight, smell, and touch – allowing us to understand our surroundings. Humans are particularly adept at linguistic reasoning and visual memory. As Generative AI (GenAI) models advance, researchers are focusing on incorporating multimodality to expand their capabilities. Traditional Large Language Models (LLMs) are limited to text input and output, neglecting other modalities like images, videos, or audio. While LLMs excel at tasks such as question answering, summarization, translation, and code generation, integrating other modalities (creating Multimodal LLMs) unlocks significant potential. For example, combining text and image data enables applications like visual question answering, image segmentation, and object detection. Adding video further enhances capabilities for advanced media analysis.

Table of Contents

• Introduction to Multimodal LLMs
• Datasets and Preprocessing
• Applications of Multimodal LLMs
  • Image Captioning
  • Information Extraction
  • Visual Interpretation and Reasoning
  • Optical Character Recognition (OCR)
  • Object Detection and Segmentation
• Architectures of Large Vision-Language Models (LVLMs)
  • Two-Tower VLMs
  • Two-Leg VLMs
  • VLMs with Image Encoder, Text Encoder & Decoder
  • VLMs with Encoder-Decoder Architecture
• Conclusion

Introduction to Multimodal LLMs

GenAI encompasses machine learning models capable of generating new content. Text-to-text models, for example, generate text from text input. However, extending LLMs with other modalities opens doors to text-to-image, text-to-video, text-to-speech, image-to-image, and image-to-video applications. These are known as Large Multimodal Models (Multimodal LLMs). Training these models involves large datasets containing text and other modalities, enabling the algorithm to learn relationships between all input types. Crucially, these models aren't restricted to single input/output types; they adapt to various modalities. This provides the system with a richer understanding of sensory input.

This article is divided into two parts: the first explores applications and architectures of multimodal LLMs, while the second (not included here) details the training of a smaller vision model.

Datasets and Preprocessing

Combining different data types to create multimodal LLMs presents challenges, particularly when handling 1D, 2D, and 3D data simultaneously. This requires a sequential, step-by-step approach with careful data curation to optimize model performance.

This discussion focuses on text and images. Images and videos, unlike text, vary in size and resolution, necessitating robust preprocessing to standardize inputs. Images, videos, prompts, and metadata must be prepared to facilitate coherent thought processes and logical consistency during inference. Models trained on text, image, and video data are called Large Vision-Language Models (LVLMs).

Applications of Multimodal LLMs

The following image (from a Qwen2-VL paper) illustrates a vision model based on the Qwen2 LLM, capable of handling various visual tasks.

The diagram below shows how a Multimodal Language Model (MMLM) processes image, text, audio, and video data to achieve various objectives. The core MMLM integrates these modalities for combined processing.

The following sections detail specific applications (code examples omitted for brevity):

1. Image Captioning: Generating textual descriptions of images.

2. Information Extraction: Retrieving specific features or data points from images (e.g., object color, text).

3. Visual Interpretation & Reasoning: Analyzing images and performing reasoning tasks based on visual information.

4. Optical Character Recognition (OCR): Extracting text from images.

5. Object Detection & Segmentation: Identifying and classifying objects within images, potentially segmenting them into distinct regions.

Architectures of Large Vision-Language Models (LVLMs)

The goal of LVLMs is to unify features from images, videos, and text. Several architectures are being explored for pre-training:

1. Two-Tower VLMs: Images and text are encoded separately and trained with a shared objective to align information from both modalities.

2. Two-Leg VLMs: Similar to two-tower, but includes a fusion layer to merge image and text features before the shared objective.

3. VLMs with Image Encoder – Text Encoder & Decoder: An image encoder processes images, while text data is processed by separate encoders and decoders, allowing for more complex interactions.

4. VLMs with Encoder-Decoder Architecture: Images are processed by an encoder, text by a decoder, with features combined (via concatenation or cross-attention) before decoding.

Conclusion

Multimodal LLMs, particularly VLMs, are trained on image-text datasets to bridge the gap between visual and textual data. They excel at visual tasks, but achieving high performance requires substantial datasets and computational resources. While capable of many visual tasks, limitations remain in complex reasoning and data extraction. Further research and development are crucial to overcome these limitations and unlock the full potential of multimodal LLMs.

References (List provided in original text)

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