Discrete Sequence Generation for Unified Image-Text Conversion
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Solution Overview
Problem
Existing methods for generating text or images based on a combination of text and images have limited application scenes and a narrow scope.
Innovation Solution
A method involving discrete encoding of original objects, followed by inputting the encoded sequences into a generative model to generate target objects of either text or image type, using a shared sequence-to-sequence framework to unify the encoding and decoding processes across different types of input objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing methods are used for generating text or images based on combination of text and images, then the generation capability is achieved, but the application scope and versatility are limited
Solution Approach 1:
The patent applies universality by designing a unified generative model that can handle multiple input types (images, text, text-image combinations) and generate multiple output types (text, images) through a single framework. The model uses a shared encoder-decoder architecture where the encoder processes different input modalities and the decoder generates different output modalities, eliminating the need for separate specialized models for each task.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the encoding and decoding parameters based on the input type. The model employs discrete encoding with codebook search that adapts to different input modalities, and the decoding process selects appropriate generation parameters based on the target output type, allowing one model to efficiently handle diverse generation tasks through parameter adaptation rather than structural changes.
2Adaptability or versatility
If a unified model is designed to handle multiple input and output types, then the versatility is improved, but the model complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the unified model into distinct functional modules: an encoder module that processes different input types, a decoder module that generates different output types, and a connection module that links them through discrete encoded sequences. This modular segmentation allows each component to be optimized for its specific function while maintaining overall system versatility, reducing the complexity burden of handling multiple modalities.
Solution Approach 2:
The patent introduces discrete encoded sequences as an intermediary representation between input and output. Instead of directly mapping between different modalities, the model encodes inputs into a unified discrete sequence space and then decodes to target modalities. This intermediary discrete representation simplifies the model architecture by providing a common language that bridges different input-output pairs, reducing the complexity of direct multi-modal mappings.
Data Source
AI summary
A method for generating a target object is provided. A first discrete encoded sequence corresponding to an original object is generated by performing discrete encoding on the original object. The original object is of an image type, a text type, or a text-image-combined type. A second discrete encode sequence is obtained by inputting the first discrete encoded sequence into a generative model. A target object is generated based on the second discrete encoded sequence. The target object is of an image type or a text type. When the original object is of the image type, the target object is of the text type. When the original object is of the text type, the target object is of the image type.