Cross-Modal Image Representation Training for Precise Text Tokens
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Solution Overview
Problem
Existing neural networks struggle to effectively map images to precise and comprehensive text representations without requiring additional supervision beyond image data, limiting their applicability in downstream tasks.
Innovation Solution
A cross-modal training approach using a text-conditional image generation neural network, such as a diffusion neural network, to generate text tokens that describe input images, enabling the image representation neural network to learn a latent space that unpacks visual-language knowledge, allowing it to generate precise and comprehensive text representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing neural networks are used to map images to text representations, then the system can process images, but the text representations lack precision and comprehensiveness without additional supervision
Solution Approach 1:
The patent introduces a text-conditional image generation model as an intermediary to bridge the image representation model and the text tokens. This intermediary model generates images from text tokens, creating a feedback loop that enables precise text representations without requiring additional supervision. The intermediary translates the abstract text tokens into visual form, allowing the system to learn precise image-text mappings through the generation process rather than direct supervision.
Solution Approach 2:
The patent implements a feedback mechanism where the text-conditional image generation model generates images from text tokens produced by the image representation model. These generated images are then compared with the original input images, and the discrepancy feeds back to improve the text token generation. This closed-loop feedback system enables the model to learn precise text representations iteratively without external supervision.
2Adaptability or versatility
If text-conditional image generation models are used during training, then comprehensive visual-language knowledge is unlocked, but the training process becomes more complex
Solution Approach 1:
The patent merges the image representation model and the text-conditional image generation model into a unified training framework. By combining these two models and training them jointly with a shared loss function, the system learns comprehensive visual-language knowledge efficiently. The merging allows gradients to flow through both models simultaneously, enabling them to co-adapt and unlock deep visual-language understanding without requiring separate complex training procedures.
3Measurement precision
If additional supervision is provided to improve text representation quality, then representation accuracy improves, but the ease of operation decreases
Solution Approach 1:
The patent enables the system to self-supervise itself by using the text-conditional image generation model to generate images from its own text tokens and comparing them with the original images. The model creates its own supervision signal through this self-generated feedback, eliminating the need for external annotated data or complex supervision mechanisms. The system serves its own training needs through the inherent capability of the generation model to produce comparable outputs.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image representation neural network.