Vision Language Models(VLMs): Exploring Multimodal AI

If we could travel back in time and tell our ancestors about technology that could seamlessly read, see, interpret, and even create on its own, they might accuse us of witchcraft. Fortunately, in our present reality, the fact this technology is actually real—and it’s called Vision Language Models (VLMs). As we explored in our previous blog post – “Applications of Vision Language Models (VLMs)” on the diverse applications of VLMs, these multimodal large language models are transforming industries and creating new possibilities in artificial intelligence. In this blog by SoftmaxAI, we’ll take a captivating journey into the heart of these multimodal AI models.

Components of Vision Language Models

Image Encoder

The image encoder is the visual backbone of VLMs. It’s responsible for extracting meaningful features from the data input images, & transforming the pixels into rich, high-dimensional representations. Many large vision-language models use advanced architectures like CLIP or modified ResNets for this purpose. The image encoder learns to identify various visual elements, from low-level features like textures and shapes to high-level concepts like objects and scenes.

Language Encoder

The language encoder processes textual input, converting words or tokens into dense vector representations. In multimodal large language models, this component often utilizes transformer-based architectures similar to those used in models like BERT or GPT. The language encoder enables the model to understand and contextualize textual information, capturing semantic nuances & syntactic structures.

Fusion Mechanism

A critical component of multimodal AI models, the cross-modal fusion module, facilitates the integration of visual and linguistic features. This module allows the model to establish relationships between visual and textual elements, enabling a comprehensive understanding of multimodal inputs. Common fusion approaches include:

Early fusion: Directly combining image and text embeddings

Late fusion: Processing image and text separately before interaction

Hybrid methods: Leveraging strengths of both early and late fusion

Multimodal Decoder

The decoder generates output based on the fused multimodal representations. In vision language models, this component can produce text (e.g., image captions, and answers to visual questions) or even visual content (in the case of text-to-image generation models). In many VLMs, this is primarily a text decoder that produces natural language responses informed by both visual and textual inputs. Some advanced models can also generate or manipulate images based on textual prompts.

Architectures of Vision Language Models(VLMs)

Vision Language Models (VLMs) are built on two main architectures:

1. Two-Stream Architecture:
   
   • Visual Stream: Processes images or videos using Convolutional Neural Networks (CNNs).
   • Textual Stream: Processes text using Transformer architectures.
   • Fusion: Combines visual and textual representations for joint understanding.
2. Single-Stream Architecture:
   • Unified Processing: Processes both image patches (visual tokens) and text tokens together using specialized transformer models.
   • No Explicit Fusion: Learns to align visual and textual information internally.

Both architectures rely on effective fusion mechanisms, such as attention mechanisms or cross-modal transformers, to understand the relationship between visual and textual data.

Top Multimodal Large Language Models

mPLUG-Owl (Alibaba DAMO Academy)

Architecture: mPLUG-Owl is a modularized multimodal large language model (MLLM) developed by Alibaba DAMO Academy. It is designed to leverage the power of large language models (LLMs) for multimodal generation by incorporating a modular learning approach. 

Training Data: Both language-only and multimodal datasets are used to jointly fine-tune the LLM and the abstractor module, enhancing the model’s ability to handle diverse tasks and modalities.

Capabilities: 

• Image Captioning: Generating accurate and informative captions for images, showcasing its understanding of visual content.
• Visual Question Answering: Answering questions about images with high accuracy, demonstrating its ability to reason about visual information.
• Text-to-Image Generation: Creating images from textual descriptions, showcasing its creativity and ability to bridge language and vision.

Strengths: mPLUG-Owl is known for its strong performance on image-text retrieval tasks and its ability to generate high-quality captions for complex images.

Flamingo (DeepMind)

Architecture: Flamingo is a vision-language model that combines a large language model with a perceptual module designed to process visual input. It employs a unique training approach called “chain-of-thought” prompting, enabling it to generate more coherent and contextually relevant responses.

Training Data: Flamingo is trained on a diverse dataset of image-text pairs, encompassing various domains and styles.

Capabilities:

• Image Captioning: Providing detailed and informative captions for images.
• Visual Dialogue: Engaging in meaningful conversations about images, demonstrating its ability to understand and respond to visual cues.
• Visual Reasoning: Solving visual puzzles and answering questions that require logical deduction based on visual information.

Strengths: Flamingo’s strengths lie in its strong visual understanding, its ability to engage in dialogue about images, and its aptitude for visual reasoning tasks.

Read More: Scope of Generative AI Development – How GenAI’s Impact on Businesses?

Unified-IO (One-for-All)

Architecture: Unified-IO is a unified model that can perform various vision and language tasks using a single architecture. It is based on the transformer model and uses a shared embedding space to represent both visual and textual data.

Training Data: Unified-IO is trained on a large-scale dataset of image-text pairs and task-specific data for different tasks.

Capabilities: Unified-IO is capable of:

• Image Captioning: Generating informative and diverse captions for images.
• Visual Question Answering: Providing accurate answers to questions about images.
• Visual Entailment: Determining whether a statement about an image is true or false.
• Image Generation: Generating images from textual descriptions.

Strengths: Unified-IO’s strengths include its unified architecture, its ability to perform multiple tasks with a single model, and its strong performance on various benchmarks.

LLaVA (Large Language and Vision Assistant)

Architecture: LLaVA is a large multimodal model designed to be an end-to-end trainable assistant for visual and language understanding. It combines a vision encoder with a language model, enabling it to process both visual and textual input.

Training Data: LLaVA is trained on a diverse dataset of image-text pairs, covering a wide range of topics and scenarios.

Capabilities:

• Image Captioning: Generating detailed and informative captions for images through a multimodal large language model.
• Visual Dialogue: Engaging in interactive conversations about images, answering questions, and providing explanations.
• Instruction Following: Completing tasks based on both visual and textual instructions as a multimodal AI model.

Strengths: LLaVA’s strengths lie in its end-to-end training approach, its ability to handle complex visual and language tasks, and its potential for interactive applications.

BLIP-2 (Salesforce)

Architecture: BLIP-2 is a vision-language model based on the Bootstrapping Language-Image Pre-training (BLIP) framework. It combines a pre-trained image encoder with a language model, allowing it to understand and generate text in the context of images.

Training Data: BLIP-2 is trained on a large-scale dataset of image-text pairs, curated from various sources.

Capabilities: 

• Image Captioning: Generating concise and accurate captions for images.
• Visual Question Answering: Answering questions about images with high precision.
• Image-Text Retrieval: Finding relevant images based on textual queries.

Strengths: BLIP-2’s strengths include its efficient training process, its ability to leverage pre-trained models, and its strong performance on visual question-answering tasks.

Kosmos-2 (Microsoft)

Architecture: Kosmos-2 is a transformer-based multimodal large language model (MLLM) that seamlessly integrates text and image processing. It employs a two-stream architecture with separate encoders for text and image inputs, followed by a cross-attention mechanism to fuse the information and a decoder to generate output.

Training Data: Kosmos-2 is trained on a massive dataset of diverse web data, including text and images. This data is curated from a wide range of sources, including websites, books, and social media platforms.

Capabilities: 

• Multimodal Grounding: Identifying and locating objects in images based on textual descriptions.
• Grounded Question Answering: Answering questions about specific image regions using bounding boxes.
• Multimodal Referring: Referring to objects or regions in images using natural language.

Strengths: Kosmos-2 excels at grounding language in the visual world, meaning it can associate words and phrases with specific regions in images.

ViLT (Vision-and-Language Transformer)

Architecture: ViLT is a transformer-based model designed for multimodal learning. It uses a single transformer architecture to process both visual and textual input, eliminating the need for separate encoders.

Training Data: ViLT is trained on a large-scale dataset of image-text pairs, learning to jointly represent visual and textual information in a shared embedding space.

Capabilities: ViLT is capable of:

• Image Captioning: Generating descriptive captions for images.
• Visual Question Answering: Answering questions about images with high accuracy.
• Image-Text Retrieval: Finding relevant images based on textual queries or vice versa.

Strengths: ViLT’s strengths include its simple and efficient architecture, its ability to learn from both labeled and unlabeled data, and its competitive performance on various multimodal tasks.

Read More: Computer Vision: Advantages and Challenges

CLIP (Contrastive Language-Image Pre-training)

Architecture: CLIP is a neural network model developed by OpenAI that learns visual concepts from natural language supervision. It consists of two encoders: a text encoder and an image encoder.

Training Data: CLIP is trained on a massive dataset of 400 million image-text pairs collected from the internet. This large-scale and diverse dataset enables CLIP to learn a wide range of visual concepts and their associated language.

Capabilities:

• Zero-Shot Image Classification: CLIP can classify images into categories without being explicitly trained on those categories. This is achieved by comparing the image’s embedding with the text embeddings of different categories and selecting the most similar one.
• Image-Text Retrieval: CLIP can retrieve images that are relevant to a given text query, or vice versa. This is done by finding the image and text embeddings that are closest in the embedding space.
• Image Captioning: CLIP can generate text descriptions for images, although this is not its primary strength.

Strengths: CLIP is a flexible model that can be applied to a variety of tasks, including image classification, image retrieval, and image captioning. 

ChatGPT-4o (OpenAI)

Architecture: ChatGPT-4o utilizes a transformer-based architecture, similar to its predecessors in the GPT series. However, it incorporates advancements in multimodal processing to handle both text and image inputs seamlessly.

Training Data: ChatGPT-4o is trained on a massive dataset of diverse web content, including text, images, and videos. This extensive training in multimodal ai models enables it to learn the intricate relationships between different modalities and generate contextually relevant responses.

Capabilities:

• Video Understanding: While still under development, ChatGPT-4o is showing promising capabilities in understanding and analyzing video content and Generative AI.
• Image Understanding and Generation: It can analyze and interpret images, generating captions, descriptions, and answers to questions about visual content.
• Textual Understanding and Generation: ChatGPT-4o excels at understanding and generating text in response to various prompts, including creative writing, summarization, translation, and code generation.

Strengths: ChatGPT-4o’s ability to handle multiple modalities makes it a versatile tool for a wide range of applications. It can be used for image captioning, video analysis, text-based conversations, and more.

Wrapping Up

From multimodal language model architecture to multimodal AI models like GPT-4, Flamingo, and Kosmos-2, we’ve witnessed the remarkable power of VLMs to bridge the gap between language and vision. As these large vision-language models continue to evolve, we can expect even more astonishing advancements in how AI perceives & interacts with the world around us.

But why just read about it when you can experience the technology firsthand? At SoftmaxAI, we’re not just geeks but also AI Solution providers. Let us be your trusted guides on your AI journey. Whether you’re looking to integrate Vision Language Models(VLMs) into your existing systems or embark on brand-new AI solutions, our team of experts is here to help you make your vision a reality. Don’t let your AI dreams become a missed-steak – contact us today for a consultation and let’s discuss the full potential of VLMs for you, together!