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Vision-Sprachmodelle und ihre Anwendungen verstehen

Learn about vision language models, how they work, and their various applications in AI. Discover how these models combine visual and language capabilities.

In a previous article, we explored how GPT-4o can understand and describe images using words. We are also seeing this capability in other new models like Google Gemini and Claude 3. Today, we’re diving deeper into this concept to explain how Vision Language Models work and how they combine visual and textual data. 

These models can be used to perform a range of impressive tasks, such as generating detailed captions for photos, answering questions about images, and even creating new visual content based on textual descriptions. By seamlessly integrating visual and linguistic information, Vision Language Models are changing how we interact with technology and understand the world around us.

How Vision Language Models Work

Before we look at where Vision Language Models (VLMs) can be used, let's understand what they are and how they work. VLMs are advanced AI models that combine the abilities of vision and language models to handle both images and text. These models take in pictures along with their text descriptions and learn to connect the two. The vision part of the model captures details from the images, while the language part understands the text. This teamwork allows VLMs to understand and analyze both images and text.

Here are the key capabilities of Vision Language Models:

  • Image Captioning: Generating descriptive text based on the content of images.
  • Visual Question Answering (VQA): Answering questions related to the content of an image.
  • Text-to-Image Generation: Creating images based on textual descriptions.
  • Image-Text Retrieval: Finding relevant images for a given text query and vice versa.
  • Multimodal Content Creation: Combining images and text to generate new content.
  • Scene Understanding and Object Detection: Identifying and categorizing objects and details within an image.
Fig 1. An example of the capabilities of a vision language model.

Next, let's explore common VLM architectures and learning techniques used by well-known models like CLIP, SimVLM, and VisualGPT.

Contrastive Learning

Contrastive learning is a technique that helps models learn by comparing differences between data points. It calculates how similar or different instances are and aims to minimize the contrastive loss, which measures these differences. It is especially useful in semi-supervised learning, where a small set of labeled examples guides the model to label new, unseen data. For example, to understand what a cat looks like, the model compares it to similar cat images and dog images. By identifying features such as facial structure, body size, and fur, contrastive learning techniques can differentiate between a cat and a dog.

Fig 2. How contrastive learning works.

CLIP is a Vision-Language Model that uses contrastive learning to match text descriptions with images. It works in three simple steps. First, it trains the parts of the model that understand both text and images. Second, it converts the categories in a dataset into text descriptions. Third, it identifies the best matching description for a given image. Thanks to this method, the CLIP model can make accurate predictions even for tasks it hasn't been specifically trained for.


PrefixLM is a Natural Language Processing (NLP) technique used for training models. It starts with part of a sentence (a prefix) and learns to predict the next word. In Vision-Language Models, PrefixLM helps the model predict the next words based on an image and a given piece of text. It uses a Vision Transformer (ViT), which breaks an image into small patches, each representing a part of the image, and processes them in sequence. 

Fig 3. An example of training a VLM that uses the PrefixLM technique.

SimVLM is a VLM that uses the PrefixLM learning technique. It uses a simpler Transformer architecture compared to earlier models but achieves better results in various tests. Its model architecture involves learning to associate images with text prefixes using a transformer encoder and then generating text using a transformer decoder. 

Multimodal Fusing with Cross-Attention

Multimodal fusing with cross-attention is a technique that improves a pre-trained Vision Language Model's ability to understand and process visual data. It works by adding cross-attention layers to the model, which allows it to pay attention to both visual and textual information at the same time. 

Here's how it works: 

  • Key objects in an image are identified and highlighted. 
  • Highlighted objects are processed by a visual encoder, translating the visual information into a format the model can understand. 
  • The visual information is passed to a decoder, which interprets the image using the knowledge of the pre-trained language model.

VisualGPT is a good example of a model that uses this technique. It includes a special feature called the self-resurrecting activation unit (SRAU), which helps the model avoid a common problem called vanishing gradients. Vanishing gradients can cause models to lose important information during training, but SRAU keeps the model's performance strong. 

Fig 4. VisualGPT model architecture.

Applications of Vision Language Models

Vision Language Models are making an impact on a variety of industries. From enhancing e-commerce platforms to making the internet more accessible, the potential uses of VLMs are exciting. Let’s explore some of these applications.

Generating Product Descriptions

When you are shopping online, you see detailed descriptions of each product, but creating those descriptions can be time-consuming. VLMs streamline this process by automating the generation of these descriptions. Online retailers can directly generate detailed and accurate descriptions from product images using Vision Language Models. 

High-quality product descriptions help search engines identify products based on specific attributes mentioned in the description. For example, a description containing "long sleeve" and "cotton neck" helps customers find a "long sleeve cotton shirt" more easily. It also helps customers find what they want quickly and, in turn, increases sales and customer satisfaction.

Fig 5. An example of an AI-generated product description. 

Generative AI models, like BLIP-2, are examples of sophisticated VLMs that can predict product attributes directly from images. BLIP-2 uses several components to understand and describe e-commerce products accurately. It starts by processing and understanding the visual aspects of the product with an image encoder. Then, a querying transformer interprets this visual information in the context of specific questions or tasks. Finally, a large language model generates detailed and accurate product descriptions.

Making the Internet More Accessible

Vision Language Models can make the internet more accessible through image captioning, especially for visually impaired individuals. Traditionally, users need to input descriptions of visual content on websites and social media. For instance, when you post on Instagram, you can add alternative text for screen readers. VLMs, however, can automate this process. 

When a VLM sees an image of a cat sitting on a sofa, it can generate the caption "A cat seated on a sofa," making the scene clear for visually impaired users. VLMs use techniques like few-shot prompting, where they learn from a few examples of image-caption pairs, and chain-of-thought prompting, which helps them break down complex scenes logically. These techniques make the generated captions more coherent and detailed.

Fig 6. Using AI to generate image captions.

To this effect, Google's "Get Image Descriptions from Google" feature in Chrome automatically generates descriptions for images without alt text. While these AI-generated descriptions may not be as detailed as those written by humans, they still provide valuable information.

Benefits and Limitations of Vision Language Models

Vision Language Models (VLMs) offer many advantages by combining visual and textual data. Some of the key benefits include:

  • Better Human-Machine Interaction: Enable systems to understand and respond to both visual and textual inputs, improving virtual assistants, chatbots, and robotics.
  • Advanced Diagnostics and Analysis: Assist in the medical field by analyzing images and generating descriptions, supporting health professionals with second opinions, and anomaly detection.
  • Interactive Storytelling and Entertainment: Generate engaging narratives by combining visual and textual inputs to improve user experiences in gaming and virtual reality.

Despite their impressive capabilities, Vision Language Models also come with certain limitations. Here are some things to keep in mind when it comes to VLMs:

  • High Computational Requirements: Training and deploying VLMs require substantial computational resources, making them costly and less accessible.
  • Data Dependency and Bias: VLMs can produce biased results if trained on non-diverse or biased datasets, which can perpetuate stereotypes and misinformation.
  • Limited Context Understanding: VLMs may struggle to understand the bigger picture or context and generate oversimplified or incorrect outputs.

Die wichtigsten Erkenntnisse

Vision Language Models have incredible potential across many fields, such as e-commerce and healthcare. By combining visual and textual data, they can drive innovation and transform industries. However, developing these technologies responsibly and ethically is essential to ensure they are used fairly. As VLMs continue to evolve, they will improve tasks like image-based search and assistive technologies. 

To keep learning about AI, connect with our community! Explore our GitHub repository to see how we are using AI to create innovative solutions in industries like manufacturing and healthcare. 🚀


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