What LLMs are capable of describing an image, or let you ask questions regarding an image?

Leon Chase

• 5 min read

To describe an image or answer questions about it, you need multimodal models—models that combine both vision and language capabilities. These models are trained on large datasets of image-text pairs and can understand visual content while generating natural language descriptions or answering questions about images.

Below is a list of some of the best Large Language Models (LLMs) and multimodal models capable of describing images or answering questions about them:

1. BLIP (Bootstrapped Language-Image Pre-training)

Overview:

  • Developer: Salesforce Research
  • Model Page: BLIP on GitHub
  • Description: BLIP is a multimodal model designed for tasks like image captioning and visual question answering (VQA). It combines vision and language pre-training to generate detailed captions and answer questions about images.

Key Features:

  • Image Captioning: Generates high-quality, contextually accurate descriptions of images.
  • Visual Question Answering (VQA): Answers questions about the content of an image.
  • Open Source: Fully open-source and freely available for use.
  • Fine-Tuning: Can be fine-tuned for specific domains or use cases.

Best For:

  • Generating detailed image descriptions.
  • Answering questions about images in natural language.

2. CLIP (Contrastive Language–Image Pre-training)

Overview:

  • Developer: OpenAI
  • Model Page: CLIP on OpenAI
  • Description: CLIP is a multimodal model that learns to associate images with text by training on a large dataset of image-caption pairs. While CLIP itself is primarily used for image classification and retrieval, it can be adapted for tasks like image captioning and VQA when combined with other models.

Key Features:

  • Zero-Shot Learning: Can generalize to new tasks without additional training.
  • Multimodal Understanding: Strong ability to match images with relevant text descriptions.
  • Open Source: Fully open-source and freely available for use.
  • Integration: Often used in conjunction with other models for caption generation or VQA.

Best For:

  • Image classification, retrieval, and zero-shot learning tasks.
  • Building custom pipelines for image description or VQA.

3. Flamingo

Overview:

  • Developer: DeepMind
  • Model Page: Flamingo on DeepMind
  • Description: Flamingo is a state-of-the-art multimodal model that combines vision and language capabilities to perform tasks like image captioning, visual question answering (VQA), and more. It is designed to handle few-shot learning scenarios, where it can adapt to new tasks with minimal examples.

Key Features:

  • Few-Shot Learning: Capable of generating captions or answering questions with very few examples.
  • Multimodal Tasks: Handles a wide range of tasks, including image captioning, VQA, and more.
  • Proprietary: Not fully open-source; access may be limited to researchers or enterprise users.

Best For:

  • Few-shot learning scenarios where the model needs to adapt quickly to new tasks.
  • Complex multimodal tasks like visual question answering and image captioning.

4. GIT (Generative Image-to-Text Transformer)

Overview:

  • Developer: Microsoft
  • Model Page: GIT on Hugging Face
  • Description: GIT is a generative model that uses a transformer architecture to generate text descriptions from images. It is trained on large datasets of image-caption pairs and can produce high-quality captions and answer questions about images.

Key Features:

  • Transformer Architecture: Uses a transformer-based architecture for generating captions.
  • High-Quality Captions: Produces detailed and contextually accurate captions.
  • Open Source: Fully open-source and freely available for use.
  • Customizability: Can be fine-tuned for specific domains or use cases.

Best For:

  • Generating detailed and accurate captions for a wide range of images.
  • Answering questions about images in natural language.

5. OFA (One For All)

Overview:

  • Developer: Alibaba DAMO Academy
  • Model Page: OFA on Hugging Face
  • Description: OFA is a multimodal pretrained model that extends M6 and achieves outstanding performance in a series of cross-modal downstream tasks, including image captioning, visual question answering (VQA), and more.

Key Features:

  • Multimodal Pre-training: Trained on large datasets of image-text pairs, enabling it to understand both visual and textual information.
  • Cross-Modal Tasks: Handles a wide range of tasks, including image captioning, VQA, and more.
  • Open Source: Fully open-source and freely available for use.
  • Customizability: Can be fine-tuned for specific domains or use cases.

Best For:

  • Generating detailed and accurate captions for a wide range of images.
  • Answering complex questions about images in natural language.

6. LXMERT

Overview:

  • Developer: UNC Chapel Hill
  • Model Page: LXMERT on GitHub
  • Description: LXMERT is a multimodal model that combines vision and language pre-training to perform tasks like visual question answering (VQA) and image captioning. It uses a transformer-based architecture to process both images and text.

Key Features:

  • Transformer Architecture: Uses transformers for both vision and language processing.
  • Multimodal Tasks: Handles tasks like VQA, image captioning, and more.
  • Open Source: Fully open-source and freely available for use.
  • Customizability: Can be fine-tuned for specific domains or use cases.

Best For:

  • Complex multimodal tasks like VQA and image captioning.
  • Applications requiring deep understanding of both images and text.

7. VisualBERT

Overview:

  • Developer: Facebook AI (now Meta)
  • Model Page: VisualBERT on GitHub
  • Description: VisualBERT is a multimodal model that extends BERT to handle both vision and language tasks. It is trained on image-caption pairs and can generate captions or answer questions about images.

Key Features:

  • BERT-Based Architecture: Extends BERT to handle multimodal tasks.
  • Visual Question Answering (VQA): Answers questions about the content of an image.
  • Open Source: Fully open-source and freely available for use.
  • Customizability: Can be fine-tuned for specific domains or use cases.

Best For:

  • Answering questions about images in natural language.
  • Generating captions for images.

8. ALIGN (A Large-scale ImaGe and Noisy-text embedding)

Overview:

  • Developer: Google Research
  • Model Page: ALIGN on Google AI Blog
  • Description: ALIGN is a dual-encoder model trained on noisy image-text pairs from the web. It is designed to align visual and textual representations, making it useful for tasks like image captioning and visual question answering (VQA).

Key Features:

  • Noisy Data Training: Trained on large-scale noisy data, enabling it to generalize well to diverse datasets.
  • Dual Encoder: Uses separate encoders for images and text, making it efficient for retrieval tasks.
  • Proprietary: Not fully open-source; access may be limited to researchers or enterprise users.

Best For:

  • Image-text alignment tasks like retrieval and captioning.
  • Handling noisy or diverse datasets.

9. ViLT (Vision-and-Language Transformer)

Overview:

  • Developer: NAVER AI Lab
  • Model Page: ViLT on GitHub
  • Description: ViLT is a lightweight multimodal model that processes both images and text using a single transformer architecture. It is designed to be efficient and fast while maintaining high performance in tasks like image captioning and visual question answering (VQA).

Key Features:

  • Efficiency: Lightweight and fast, making it suitable for real-time applications.
  • Transformer Architecture: Uses a single transformer for both vision and language tasks.
  • Open Source: Fully open-source and freely available for use.
  • Customizability: Can be fine-tuned for specific domains or use cases.

Best For:

  • Real-time applications where efficiency and speed are critical.
  • Generating captions or answering questions about images in resource-constrained environments.

10. Florence

Overview:

  • Developer: Microsoft
  • Model Page: Florence on GitHub
  • Description: Florence is a multimodal model that supports a wide range of vision-and-language tasks, including image captioning, visual question answering (VQA), and object detection. It is designed to handle complex multimodal tasks with high accuracy.

Key Features:

  • Multimodal Tasks: Supports tasks like image captioning, VQA, object detection, and more.
  • High Accuracy: Achieves state-of-the-art performance on many benchmarks.
  • Open Source: Fully open-source and freely available for use.
  • Customizability: Can be fine-tuned for specific domains or use cases.

Best For:

  • Complex multimodal tasks like VQA, image captioning, and object detection.
  • Applications requiring high accuracy and versatility.

Conclusion

The best multimodal models for describing images or answering questions about them depend on your specific needs:

  • General-Purpose Captioning & VQA: BLIP, GIT, and OFA are excellent choices for generating detailed captions and answering questions.
  • Efficiency: ViLT is ideal for lightweight, real-time applications.
  • Few-Shot Learning: Flamingo excels in scenarios where the model needs to adapt quickly to new tasks.
  • Image-Text Alignment: CLIP and ALIGN are great for tasks like image retrieval and classification.

By exploring these models, you can find the right tool to enhance your image description and visual question-answering workflows, whether you're looking for real-time assistance, open-source flexibility, or advanced reasoning capabilities.