Introduction to Visual Language Models (VLMs): The Future of Computer Vision

Achieving a 28% Boost in Multimodal Image Search Accuracy with VLMs

Until recently, artificial intelligence models were narrowly focused—either excelling in understanding text or interpreting images, but rarely both. This siloed approach limited their potential to process and connect different types of data.

The development of general-purpose language models, like GPTs, marked a significant leap forward. These models transitioned AI from task-specific systems to powerful, versatile tools capable of handling a wide range of language-driven tasks. Yet, despite their advancements, language models and computer vision systems evolved separately, like having the ability to hear without seeing—or vice versa.

Visual Language Models (VLMs) bridge this gap, combining the capabilities of language and vision to create multimodal systems. In this article, we’ll explore VLMs’ architecture, training methods, challenges, and transformative potential in fields like image search. We’ll also examine how implementing VLMs revolutionized an AI-powered search engine.

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What Are Visual Language Models (VLMs)?

VLMs represent the next step in the evolution of AI: multimodal models capable of processing multiple data types, including text, images, audio, and video.

Why Multimodal Models?

The rise of general-purpose approaches has outpaced narrow, specialized systems in recent years. Here’s why:

1. Unified Solutions: Modern language models (LLMs) can handle multiple tasks—such as translation, summarization, and speech tagging—within a single system, reducing the need for task-specific models.
2. Scalability: Training multimodal models exponentially increases the available data by integrating different formats like text and images. This addresses the limitations of finite text datasets.
3. Enhanced Performance: Combining modalities improves a model’s understanding of each, much like humans benefit from using multiple senses simultaneously.

At their core, VLMs receive input in the form of images and text (called “instructs”) and produce textual responses. Their range of applications includes image classification, description, interpretation, and more.

For example:

• Describe the image: “What’s happening in this picture?”
• Interpret context: “What does this road sign mean?”
• Perform tasks: “Solve the math problem shown in the image.”

These tasks showcase VLMs’ ability to tackle a variety of challenges, including zero-shot and one-shot scenarios, where minimal prior training is required.

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How Do VLMs Work?

Core Components

VLMs typically consist of three main components:

1. LLM (Language Model): Processes and generates text (e.g., YandexGPT).
2. Image Encoder: Interprets visual input using models like CNNs or Vision Transformers.
3. Adapter: Acts as a bridge between the LLM and the image encoder, enabling seamless communication.

Workflow

1. The image encoder processes the visual input.
2. The adapter transforms the image encoder’s output into a format compatible with the LLM.
3. The LLM processes both the adapted image data and the text instruct to generate a response.

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Adapters: The Key to Multimodality

Adapters facilitate interaction between the image encoder and LLM. There are two primary types:

1. Prompt-Based Adapters
   • Convert image encoder outputs into token sequences for the LLM.
   • Simple and effective but consume significant LLM input context.
2. Cross-Attention-Based Adapters
   • Integrate image data into the LLM’s cross-attention blocks.
   • Require more parameters but preserve the LLM’s input context.

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Training VLMs

Pre-Training

VLMs are built on pre-trained LLMs and image encoders. Pre-training focuses on linking text and image modalities, as well as embedding world knowledge from visual data.

Three types of data are used:

1. Interleaved Pre-Training: Combines text and images from web documents.
2. Image-Text Pair Pre-Training: Focuses on specific tasks like image captioning using labeled datasets.
3. Instruct-Based Pre-Training: Uses image-text-instruct triplets to train models in real-world scenarios.

Alignment

Alignment fine-tunes VLMs for high-quality responses. This involves:

• Supervised Fine-Tuning (SFT): Trains on curated datasets with clear structure and reasoning.
• Reinforcement Learning (optional): Refines performance through reward-based adjustments.

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Quality Evaluation

Evaluating VLM performance involves:

1. Benchmark Metrics: Measures accuracy on standardized datasets.
2. Side-by-Side (SBS) Evaluations: Human assessors compare responses based on grammar, readability, relevance, and logical consistency.

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Revolutionizing Image Search with VLMs

Incorporating VLMs into search engines transforms user experience by integrating text and image inputs.

Previous Pipeline

1. User submits an image and text query.
2. Visual search analyzes the image, generating tags and metadata.
3. A rephraser (LLM) refines the query using visual search data.
4. Text-based search retrieves documents, and an LLM generates the final response.

VLM-Powered Pipeline

With VLMs, image and text inputs are processed together, creating a streamlined, accurate system that outperforms traditional pipelines by 28%.

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Conclusion

Visual Language Models are a game-changer for AI, breaking down barriers between language and vision. From multimodal search engines to advanced problem-solving, VLMs unlock new possibilities in computer vision and beyond.

The future of AI is multimodal—and VLMs are leading the way.

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