Image Caption Model Learning with Shared Crossmodal Translation
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Solution Overview
Problem
Conventional image caption generation models require a large amount of labeled learning data, which is costly and difficult to collect, leading to insufficient performance.
Innovation Solution
Utilize machine translation and paraphrase generation data to learn a unified function for image caption generation, sharing model parameters with these tasks to enhance performance with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large amount of labeled learning data is used for image caption generation, then the model performance is improved, but the data collection cost and complexity increase significantly
Solution Approach 1:
The patent combines image caption generation task with machine translation task into a unified model framework. The model simultaneously learns to generate captions for images and translate text pairs, sharing common parameters between the two tasks. This merging allows the system to leverage machine translation data (which is abundant and easier to collect) to improve image caption generation performance, thereby reducing the need for large amounts of manually annotated image-caption data.
Solution Approach 2:
The patent creates a universal model that performs multiple functions: image caption generation and machine translation. By designing the model to handle both tasks with shared parameters, it achieves multi-functionality. This universal approach allows the system to utilize diverse data sources (image-caption pairs and text translation pairs) to improve performance on the target task of image caption generation, effectively reducing the data collection burden for this specific task.
2Quantity of substance
If machine translation data is used to improve image caption generation, then the amount of learning data required is reduced, but the model complexity increases
Solution Approach 1:
The patent merges the image caption generation model with a machine translation model into a unified architecture. The model integrates components for both tasks, allowing it to process and learn from both image-caption pairs and text translation pairs. This merging enables the system to reduce the quantity of learning data needed for image caption generation by leveraging the abundant machine translation data, while the integrated architecture manages model complexity through shared parameters and coordinated processing.
Data Source
AI summary
An image caption generation model learning apparatus uses, as inputs, pair data of an image that is learning data for image caption generation and text data that is a caption describing the image and pair data of a first language text and a second language text that are machine translation data; and learns an image parameter that is a model parameter for image hidden information generation, a text parameter that is a model parameter for text hidden information generation, a crossmodal parameter that is a model parameter for crossmodal invariant information embedment, and an output parameter that is a model parameter for text generation.