Image Captioning Model Training With Viewpoint-Aligned Features
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Solution Overview
Problem
Existing image captioning technologies struggle with inaccuracies due to viewpoint changes between images, leading to erroneous determinations of changes when there are none, particularly caused by changes in camera position or angle.
Innovation Solution
A method of training an image captioning model that includes extracting features from images, encoding viewpoint information, obtaining viewpoint-aligned features, and generating captions by fusing these features to accurately identify differences while accounting for viewpoint changes using a neural network-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If viewpoint information is not encoded, then the image captioning model can process images quickly, but the model produces inaccurate captions when viewpoint changes occur between images
Solution Approach 1:
The patent applies preliminary action by encoding viewpoint information from reference images into the query features before the main captioning process. This pre-processing step aligns the query features with the target image's viewpoint, enabling the model to accurately distinguish actual changes from viewpoint differences without adding complex post-processing steps.
Solution Approach 2:
The patent introduces an intermediary mechanism by using viewpoint-encoded query features as a mediator between the reference image and target image comparison. This intermediary representation captures viewpoint information that guides the attention mechanism to focus on relevant features, improving captioning accuracy while maintaining model efficiency.
2Reliability
If viewpoint-aligned features are obtained through feature fusion, then the model can accurately distinguish actual changes from viewpoint differences, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary viewpoint encoding by incorporating reference image features into the query representation before the main comparison process. This pre-alignment reduces the need for extensive computational adjustments during inference, improving reliability while controlling computational energy consumption.
Solution Approach 2:
The patent replaces complex mechanical-like operations with neural network-based feature fusion and attention mechanisms. Instead of traditional image registration and alignment procedures, the model uses learned feature transformations to achieve viewpoint alignment, reducing computational overhead while maintaining accuracy.
Data Source
AI summary
A method of training an image captioning model includes: extracting a feature of a first image from the first image and extracting a feature of a second image from the second image; by encoding viewpoint information based on the feature of the first image and the feature of the second image, obtaining a first image's feature including viewpoint information and a second image's feature including the viewpoint information; obtaining a first image's viewpoint-aligned feature and a second image's viewpoint-aligned feature, based on the first image's feature including the viewpoint information and the second image's feature including the viewpoint information; and generating a caption describing a difference between the first image and the second image, based on the first image's viewpoint-aligned feature and the second image's viewpoint-aligned feature.