De-Diffusion Makes Text a Strong Cross-Modal Interface (2311.00618v1)

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 LLMs with few-shot examples.

• The paper demonstrates that text can serve as a powerful cross-modal interface by encoding images into semantically-rich text tokens.
• The method uses a pre-trained text-to-image diffusion model as a decoder, achieving superior image reconstruction compared to conventional captions.
• The approach enables few-shot vision-language tasks by interfacing with large language models without any additional training.

The paper "De-Diffusion Makes Text a Strong Cross-Modal Interface" (2311.00618) presents a method to utilize text as a potent cross-modal interface rather than relying on deep embeddings. The core idea is to encode images into text using an autoencoder with a pre-trained text-to-image diffusion model as the decoder, dubbed the De-Diffusion technique. This approach enables text to act not only as an interpretable and flexible interface between various modalities but also to support comprehensive representation useful in multiple tasks like image synthesis and vision-language applications.

Key Contributions:

1. Cross-Modal Interface:
   • The main premise is that text can serve as an effective cross-modal interface by encoding an image into a sequence of text tokens, producing a "scrambled caption" that maintains the semantic richness present in the original image.
2. De-Diffusion Technique:
   • Employs a pre-trained text-to-image diffusion model, which is a generative model, as the decoder. The encoder maps image features to text, optimizing the text so that decoding reconstructs the original image.
3. Flexible Applications:
   • The text generated by the De-Diffusion method can directly interface with off-the-shelf LLMs such as PaLM 2, enabling open-ended vision-language tasks through few-shot learning without additional training.
4. Quantitative and Qualitative Evaluations:
   • Demonstrates superior performance in reconstructing images from De-Diffusion text using third-party diffusion models like Stable Diffusion, showing better FID scores compared to human captions and state-of-the-art captioning methods.
   • Showcases the effectiveness in open-ended visual question answering (VQA), surpassing capabilities of models like Flamingo in few-shot settings.
5. Strong Few-Shot Learning Capability:
   • The De-Diffusion model's encoded text allows LLMs to perform few-shot learning on vision-language tasks without the need for retraining, showing robust generalization to varied tasks such as multi-modal VQA and image captioning.

Technical Specifics:

• Image-to-Text Encoder: Comprises of an attentional pooler applied to features from a vision backbone (pre-trained or trained from scratch). Text tokens are mapped to the vocabulary space used by CLIP's text encoder.
• Training and Optimization:
  • Training uses image-only datasets, leveraging an unsupervised autoencoding approach to produce comprehensive textual representation. It utilizes Gumbel-softmax for discrete token relaxation and sophisticated annealing schedules to ensure effective training.
• Ablation Studies:
  • Conducted to evaluate different design choices like number of text tokens, vocab excluding punctuation, and model architectures for image feature extraction. Results indicate that pre-trained models significantly improve performance and generalization.

Applications Demonstrated:

• Text-to-Image Reconstruction:
  • Experiments show De-Diffusion text's transferability across different generative models, enabling consistent high-quality image synthesis.
• Multi-Modal Dialogue:
  • Enables text-only chatbots like ChatGPT to engage with image context, providing grounded dialogue tasks by using De-Diffusion-generated text prompts.
• One-Shot Image Classification:
  • Shows efficacy in classification tasks by transforming the images into textual descriptions which are then used by LLMs for prediction.

The paper articulates a compelling case for the use of text as a versatile and robust interface, grounded by quantitative improvements and qualitative expansions in interacting across modalities. However, the paper focuses more on the practical validity and the potential of De-Diffusion text rather than theoretical exploration of why text might outperform other deep embedding interfaces. Nevertheless, this work opens avenues for leveraging LLMs in tasks that traditionally require complex modality-specific embeddings.