Adaptive Cycle Consistency for Low-Resource Image Captioning
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Solution Overview
Problem
Low-resource languages face challenges in image captioning due to limited training data, leading to low accuracy and precision, as existing models overfit or underfit, and the cost of creating caption datasets is prohibitively expensive, especially for uncommon languages.
Innovation Solution
The method involves encoding images using a CNN encoder and generating high-resource captions, which are then encoded and used to generate low-resource captions through a trained low-resource decoder, with adaptive cycle consistency constraints applied to attention weights to alleviate language misalignment and improve captioning performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image captioning models are used for low-resource languages, then the model structure remains simple, but accuracy and precision deteriorate due to limited training data causing overfitting or underfitting
Solution Approach 1:
The patent introduces high-resource language captions as an intermediary to bridge the gap between limited low-resource data and the need for accurate captioning. The model first generates captions in high-resource languages using abundant training data, then translates them to low-resource languages, effectively using the high-resource language as a mediator to overcome data scarcity.
Solution Approach 2:
The captioning task is segmented into multiple stages: image encoding, high-resource caption generation, translation to low-resource language, and final caption assembly. This segmentation allows each component to be optimized independently, with the high-resource language stage benefiting from abundant data while the low-resource stage requires minimal data.
2Measurement precision
If more training data is collected for low-resource languages, then captioning accuracy improves, but the cost of creating caption datasets becomes prohibitively expensive
Solution Approach 1:
Instead of creating expensive new caption datasets for low-resource languages, the patent copies and translates captions from high-resource languages. This copying approach leverages existing high-quality captions through translation, avoiding the costly process of manual annotation while maintaining caption quality.
Solution Approach 2:
The patent changes the language parameter from low-resource to high-resource language during the caption generation process. By generating captions in high-resource languages first and then translating, the system exploits the abundance of high-resource language data without incurring the costs of collecting low-resource language data.
3Reliability
If adaptive cycle consistency constraints are applied to attention weights, then language misalignment is reduced and captioning performance improves, but computational complexity increases
Solution Approach 1:
The patent implements feedback through adaptive cycle consistency constraints that monitor and adjust attention weights during the translation and caption generation process. The cycle consistency mechanism provides feedback signals that guide the attention mechanism to maintain alignment between source and target languages, improving reliability through continuous adjustment.
Data Source
AI summary
In an approach to improving the image captioning performance of low-resource languages by leveraging multimodal inputs, one or more computer processors encode an image utilizing an image encoder, wherein the image is contained within a triplet comprising the image, one or more high-resource captions, and one or more low-resource captions. The one or more computer processors generate one or more high-resource captions utilizing the encoded image and the triplet inputted into a high-resource decoder. The one or more computer processors encode the one or more generated high-resource captions utilizing a high-resource encoder. The one or more computer processors add adaptive cycle consistency constraints on a set of calculated attention weights associated the triplet. The one or more computer processors generate one or more low-resource captions by simultaneously inputting the encoded image, the encoded high-resource caption, and the triplet into a trained low-resource decoder.