Cross-Attention Image Description System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for generating descriptions of digital image pairs are dataset-specific and unable to effectively describe relationships and differences between images, as they rely on specific preprocessing techniques that limit their ability to handle diverse and implicit visual relationships across multiple datasets.
Innovation Solution
A computing device implements a description system that generates feature maps for source and target images, flattens them into sequences, and uses cross-attention to determine visual relationships, enabling the generation of natural language descriptions of image transformations, differences, and objects without relying on specific datasets, thereby addressing the limitations of conventional systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems use dataset-specific preprocessing techniques (masking and clustering), then they can achieve promising results on specific datasets, but they become limited to those datasets and unable to handle diverse visual relationships across multiple datasets
Solution Approach 1:
The patent applies universality by developing a cross-attention mechanism that can process multiple types of visual relationships across different datasets without requiring dataset-specific preprocessing. The system generates feature maps and uses cross-attention between source and target image features to describe various relationships (spatial, temporal, causal) universally across different image pairs and datasets, eliminating the need for dataset-specific clustering and masking techniques.
2Measurement precision
If conventional systems focus on localized relationships through dataset-specific preprocessing, then they can approximate object-level differences, but they fail to capture complex visual transformations and implicit relationships between images
Solution Approach 1:
The patent transitions from localized 2D spatial relationships to a global relationship space by using cross-attention. Instead of processing only local pixel-level differences through masking, the system creates feature maps that capture global semantic information and uses cross-attention to establish relationships between corresponding features in source and target images across the entire image space, enabling description of complex transformations and implicit relationships.
3Ease of manufacture
If conventional systems avoid the additional problem of determining relationships between images by using dataset-specific preprocessing, then they can simplify processing, but they cannot generate descriptions of visual transformations, image editing operations, or differences between images
Solution Approach 1:
The patent performs preliminary action by generating feature maps for both source and target images before applying cross-attention. This preliminary feature extraction step simplifies the subsequent relationship description task by pre-processing the images into meaningful feature representations, making the cross-attention mechanism more efficient at identifying and describing visual relationships, transformations, and differences.
Data Source
AI summary
In implementations of generating descriptions of image relationships, a computing device implements a description system which receives a source digital image and a target digital image. The description system generates a source feature sequence from the source digital image and a target feature sequence from the target digital image. A visual relationship between the source digital image and the target digital image is determined by using cross-attention between the source feature sequence and the target feature sequence. The system generates a description of a visual transformation between the source digital image and the target digital image based on the visual relationship.