De-Diffusion Makes Text a Strong Cross-Modal Interface
akhaliq
Abstract
We demonstrate text as a strong cross-modal interface. Rather than relying on deep embeddings to connect image and language as the interface representation, our approach represents an image as text, from which we enjoy the interpretability and flexibility inherent to natural language. We employ an autoencoder that uses a pre-trained text-to-image diffusion model for decoding. The encoder is trained to transform an input image into text, which is then fed into the fixed text-to-image diffusion decoder to reconstruct the original input -- a process we term De-Diffusion. Experiments validate both the precision and comprehensiveness of De-Diffusion text representing images, such that it can be readily ingested by off-the-shelf text-to-image tools and LLMs for diverse multi-modal tasks. For example, a single De-Diffusion model can generalize to provide transferable prompts for different text-to-image tools, and also achieves a new state of the art on open-ended vision-language tasks by simply prompting large language models with few-shot examples.
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This seems very limiting. It would exclude more advanced vision functions such as detection/segmentation etc. The only thing it seems to be useful for is image summarization.
This seems very limiting. It would exclude more advanced vision functions such as detection/segmentation etc. The only thing it seems to be useful for is image summarization.
Thanks for the valuable feedback! We agree that the current version of De-Diffusion can only be applied for image-level tasks, from image classification, VQA to summarization. But we believe the framework of reversing a pre-trained text-to-image generative model is general and promising! For example, the image-level text latent of the auto-encoder can be extended to patch-level text latent, then you will have a model comprehensively describing each patch!
It's interesting. If you can pull it off, I supposed it's merits could be in simplifying the how multiple models talk to each other.
It's interesting. If you can pull it off, I supposed it's merits could be in simplifying the how multiple models talk to each other.
Yes, "simplifying the how multiple models talk to each other" is definitely what we want to achieve!
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This seems very limiting. It would exclude more advanced vision functions such as detection/segmentation etc. The only thing it seems to be useful for is image summarization.
I was a little confused too, but asked gpt 4 to give me examples of where this could be useful:
Social Media Platforms
Content Moderation: Platforms like Facebook or Twitter could use De-Diffusion to convert images into text for easier moderation, allowing for the detection of inappropriate content through text analysis.
Accessibility Features: Improve accessibility by providing detailed image descriptions for visually impaired users, enhancing the user experience on platforms like Instagram.
E-commerce
Product Searches: In online marketplaces like Amazon, De-Diffusion could translate product images into descriptive texts that can be indexed for more nuanced search capabilities, allowing users to find products through detailed image descriptions.
Customer Service Chatbots: Enhance chatbot interactions on platforms like Shopify by allowing them to understand and reference products' visual details in customer service inquiries.
Educational Software Learning Tools: In educational platforms like Khan Academy, De-Diffusion can provide detailed descriptions of diagrams and images, making educational content more accessible and understandable through text.
Interactive Textbooks: Enhance e-textbooks with the ability to describe images in detail, aiding students who rely on screen readers or prefer text-based learning.
Content Creation and Management
Stock Photo Libraries: Services like Adobe Stock could use De-Diffusion to generate better metadata for images, improving searchability and categorization.
Digital Asset Management: Improve the organization of visual assets in DAM systems by using text-based descriptions, aiding in retrieval and usage of digital content.
Smart Home Devices
Voice Assistants: Devices like Amazon Echo with a screen could use De-Diffusion to describe images to users, making interactions more informative and engaging.
Security Cameras: Integrate with home security systems like Ring to provide homeowners with textual descriptions of security footage for quick understanding of visual data.
Automotive Technology
Driver Assistance Systems: In vehicles with advanced driver-assistance systems (ADAS), De-Diffusion could provide descriptions of road conditions or obstacles, integrating with the vehicle's display systems or audio output for driver alerts.
Healthcare
Diagnostic Tools: Aid diagnostic imaging software by translating medical images (like X-rays or MRIs) into descriptive text, which can then be analyzed by AI for preliminary diagnoses.
Gaming and Virtual Reality
Game Development: Use in game development to convert visual elements into text for dynamic storytelling or to create descriptive captions for accessibility.
VR Navigation: Help visually impaired users navigate VR environments with descriptive audio cues generated from text descriptions of the visual scene.
Any plans for open sourcing?
Any plans for open sourcing?
A recent visit to the authors' GitHub page indicates that the source code will be released soon. Looking forward to open source!
https://dediffusion.github.io/
Any plans for open sourcing?
A recent visit to the authors' GitHub page indicates that the source code will be released soon. Looking forward to open source!
https://dediffusion.github.io/
Your recent visit yields the same results as mine 2 months ago :)
Any plans for open sourcing?
A recent visit to the authors' GitHub page indicates that the source code will be released soon. Looking forward to open source!
https://dediffusion.github.io/Your recent visit yields the same results as mine 2 months ago :)
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De-Diffusion: Transforming Images into Text for Multi-Modal AI
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