Convex Combination Data Augmentation for Image Captioning
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Solution Overview
Problem
Image caption generation models require substantial training data, which is often unavailable or prohibitively expensive, especially for uncommon languages, leading to poor performance due to overfitting and underfitting, and increased costs in creating and maintaining caption datasets.
Innovation Solution
The method involves sampling a ratio lambda from a probability distribution for pairs of datapoints in the dataset to generate new datapoints through convex combination, incorporating linear interpolation of features, thereby augmenting the training dataset and reducing the need for extensive data collection and annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive training data is collected and annotated for image captioning models, then model accuracy and precision improve, but costs and resources required for dataset creation and maintenance increase
Solution Approach 1:
The patent generates synthetic training data by copying and transforming existing image-caption pairs through convex combination. New training examples are created by interpolating between existing datapoints, effectively copying the structure of real data while generating variations that expand the dataset without requiring additional real-world annotations.
Solution Approach 2:
The patent replaces expensive manual annotation processes with automated synthetic data generation. Instead of investing resources in collecting and annotating extensive real-world data, the system uses computational methods to generate training data that is cheaper to produce and can be readily discarded or regenerated as needed.
2Reliability
If more training data is available for uncommon languages, then model performance improves, but data collection and annotation costs increase
Solution Approach 1:
The system performs self-service by generating its own training data through convex combination of existing examples. The model uses existing multilingual image-caption pairs to automatically create synthetic training data for uncommon languages, eliminating the need for external data collection and manual annotation efforts.
Solution Approach 2:
For uncommon languages with limited real data, the patent generates synthetic training examples that are inexpensive to produce computationally. These synthetic datapoints serve as reliable training material without requiring expensive manual annotation, enabling model training for languages that would otherwise be infeasible.
3Ease of manufacture
If existing training data is used directly, then dataset creation costs are reduced, but model accuracy deteriorates due to overfitting and underfitting
Solution Approach 1:
The patent transforms existing training data by changing parameters through convex combination. By interpolating between existing datapoints using random coefficients, the system creates varied synthetic examples that prevent overfitting to specific patterns while maintaining the underlying data distribution, thereby improving generalization accuracy.
Solution Approach 2:
The system performs preliminary data transformation through convex combination before training. By pre-generating diverse synthetic examples from existing data, the model receives enriched training material that prevents both overfitting and underfitting, improving accuracy without requiring additional real data collection.
Data Source
AI summary
In an approach to augmenting caption datasets, one or more computer processors sample a ratio lambda from a probability distribution based on a pair of datapoints contained in a dataset, wherein each datapoint in the pair of datapoints comprises an image and an associated caption; extend the dataset by generating one or more new datapoints based on the sampled ratio lambda for each pair of datapoints in the dataset, wherein the sampled ratio lambda incorporates an interpolation of features associated with the pair of datapoints into the generated one or more new datapoints; identify one or more objects contained within a subsequent image utilizing an image model trained utilizing the extended dataset; generate a subsequent caption for one or more identified objects contained within the subsequent image utilizing a language generating model trained utilizing the extended dataset.