Vision-Language Caption Training With Synthetic Unpaired Captions
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Solution Overview
Problem
Conventional machine learning systems for image captioning rely heavily on high-volume image-caption pairs, which are challenging to collect and often contain noisy data, leading to the generation of low-quality captions that fail to capture essential image information.
Innovation Solution
A machine learning model that generates augmented training captions using unpaired data and iteratively trains a captioning model with contrastive and caption loss functions, incorporating synthetic captions derived from web data to enhance alignment between images and text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually intensive methods are used for creating training data, then data quality can be controlled, but productivity is reduced and large-scale caption generation is not achievable
Solution Approach 1:
The system uses the captioning model itself to generate synthetic captions that serve as training data, creating a self-reinforcing loop where the model generates data that improves its own performance. The generated captions are then used to retrain and refine the model, eliminating the need for manual data creation while maintaining quality through iterative self-improvement.
Solution Approach 2:
The system performs preliminary data generation by creating synthetic captions before actual training occurs. By pre-generating large volumes of training data using the captioning model, the system prepares extensive training material in advance, enabling large-scale training without manual intervention and resolving the contradiction between data quality control and productivity.
2Quantity of substance
If high-volume image-caption pairs are collected, then training data quantity increases, but data quality deteriorates due to noisy data
Solution Approach 1:
Instead of collecting real image-caption pairs from the web which contain noise, the system creates synthetic copies of captions generated by the model itself. These synthetic captions are paired with actual images to create clean, high-quality training data that maintains both quantity and quality, avoiding the noise inherent in manually collected or web-scraped data.
3Productivity
If the captioning model is trained on web-scraped captions, then productivity increases with large-scale data, but measurement precision decreases due to noisy captions
Solution Approach 1:
The system introduces synthetic captions as an intermediary between the captioning model and the training process. Instead of directly using noisy web-scraped captions, the model generates cleaned, synthetic versions that serve as high-quality training signals, maintaining both the scalability of large-scale training and the precision of accurate captioning through this intermediary layer.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for caption generation includes obtaining training data including an input image depicting a scene and training, using the training data, a captioning model to generate a text caption describing the scene. Training the captioning model comprises training an image encoder of the captioning model to encode the input image to obtain an image embedding representing the scene and training a language decoder of the captioning model to generate the text caption based on the image embedding, wherein the captioning model is trained based on an output of the language decoder.