Cross-Modal Image Difference Captioning Without Pre-Segmentation
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Solution Overview
Problem
Conventional systems struggle to accurately identify and explain differences between digital images, particularly in cases of varying image quality, noise, and geometric transformations, often requiring precise pre-segmentation and being limited to near-identical views.
Innovation Solution
An image difference captioning system utilizing a cross-modal neural network generates separate feature maps for digital images, converts them to a feature space using a linear projection layer, and combines these with a large language machine-learning model to produce accurate difference captions, trained through a pairwise comparison with target captions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image difference identification systems are used, then they can identify differences between near-identical views, but they require precise pre-segmentation and are limited to specific image conditions
Solution Approach 1:
The patent extracts and removes the pre-segmentation step from the conventional image difference identification system. Instead of requiring precise pre-segmentation, the system directly processes entire images through a cross-modal neural network that automatically learns relevant features and differences, eliminating the need for manual or algorithmic pre-segmentation while maintaining or improving accuracy
Solution Approach 2:
The patent creates a universal image difference captioning system that handles multiple image conditions (varying quality, noise, geometric transformations) through a single integrated cross-modal neural network. This multi-functional system replaces multiple specialized conventional systems that each required specific pre-segmentation procedures for different image types
2Loss of information
If conventional image difference systems are used, then they can provide basic difference detection, but they cannot accurately explain differences in human-readable format
Solution Approach 1:
The patent introduces a language model as an intermediary component that translates visual image differences into human-readable caption explanations. The cross-modal neural network extracts visual features, and the language model converts these features into natural language descriptions of differences, bridging the gap between image analysis and human communication
Solution Approach 2:
The patent creates a composite system combining computer vision capabilities (image encoding, feature extraction) with natural language processing capabilities (caption generation, difference explanation). This composite architecture integrates multiple specialized components into a unified system that provides both accurate difference detection and human-readable explanations
3Adaptability or versatility
If systems process images with varying quality and noise, then they can handle diverse image conditions, but conventional systems fail to maintain accuracy
Solution Approach 1:
The patent implements a dynamic feature extraction approach where the cross-modal neural network adaptively adjusts its processing based on input image characteristics. The system dynamically learns to weight and prioritize different visual features depending on image quality, noise levels, and geometric transformations, maintaining accuracy across varying conditions rather than using fixed processing parameters
Solution Approach 2:
The patent employs parameter changes in the neural network processing to handle diverse image conditions. The system modifies its internal representation parameters and feature weighting based on the specific characteristics of each image pair, allowing it to maintain high accuracy whether processing high-quality clean images or low-quality noisy images with geometric transformations
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating difference captions indicating detected differences in digital image pairs. The disclosed system generates a first feature map of a first digital image and a second feature map of a second digital image. The disclosed system converts, utilizing a linear projection neural network, the first feature map to a first modified feature map in a feature space corresponding to a large language machine-learning model. The disclosed system also converts, utilizing the linear projection neural network layer, the second feature map to a second modified feature map in the feature space corresponding to the large language machine-learning model. The disclosed system further generates, utilizing the large language machine-learning model, a difference caption indicating a difference between the first digital image and the second digital image from a combination of the first modified feature map and the second modified feature map.