Image Text Description Generation via Semantic Alignment
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Solution Overview
Problem
Conventional multimodal large language models struggle with generating accurate text descriptions for images due to the presence of low-quality data in image-text datasets, leading to poor performance and inaccurate descriptions.
Innovation Solution
The approach involves semantically aligning a visual encoder with a text encoder, which is then used to train a conversion model and a language model. This alignment allows the visual encoder to convert image features into a space compatible with the language model, enabling the generation of accurate text descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional multimodal large language models process image-text data directly, then the model can handle diverse input types, but low-quality data in the dataset leads to inaccurate text descriptions
Solution Approach 1:
The patent extracts and removes low-quality data from the image-text dataset through data filtering and cleaning processes. Only high-quality data pairs are retained for training the conversion model and language model, thereby eliminating the harmful effect of low-quality data while preserving the multimodal processing capability
Solution Approach 2:
The patent introduces a conversion model as an intermediary component between the visual encoder and the language model. This conversion model transforms visual features into a representation space compatible with the language model, enabling accurate text generation while filtering out low-quality data through the intermediary processing layer
2Ease of manufacture
If the visual encoder and language model are trained independently, then the training process is simpler, but the semantic alignment between visual and text representations is insufficient
Solution Approach 1:
The patent merges the training processes by using a shared conversion model that is trained on both visual features and text representations. This unified training approach simultaneously achieves semantic alignment between modalities while maintaining training simplicity through a single integrated model structure
Solution Approach 2:
The patent changes the parameter space by transforming visual features through the conversion model into a representation space that is semantically aligned with the language model's text space. This parameter transformation enables direct semantic alignment while keeping the training process manageable through controlled feature space manipulation
Data Source
AI summary
Embodiments of the present disclosure relate to a method and apparatus for generating a text description for an image, an electronic device, and a medium. The method includes generating a first feature of the image through a visual encoder, where a text encoder semantically aligned with the visual encoder is used to train a conversion model and a language model. The method further includes converting the first feature into a second feature through the conversion model, where the first feature and the second feature correspond to different feature spaces. In addition, the method also includes generating, by the language model, a text description for the image based on the second feature.