Generative Image Acquisition for Complex Query Completion
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Solution Overview
Problem
Existing image search technologies struggle to provide sufficient images that fully satisfy complex queries with multiple objects and attributes, as they rely solely on indexing existing images and often fail to find or generate images that include all specified elements.
Innovation Solution
Combining image search technology with image generation technology, using deep learning-based models like Generative Adversarial Networks (GANs) to generate images that complete or modify existing images to meet the query requirements, enhancing the search results with semantically accurate images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image search technology relies solely on indexing existing images, then the system complexity remains low, but the ability to satisfy complex queries with multiple objects and attributes deteriorates
Solution Approach 1:
The patent combines image search technology with image generation technology into a unified system. The image search module retrieves relevant images while the image generation module creates complementary images, together providing comprehensive results that satisfy complex queries with multiple objects and attributes.
Solution Approach 2:
The system performs multiple functions: it searches for existing images, generates new images, detects missing components, and synthesizes results. This multi-functional approach enables the system to handle diverse query types and deliver accurate results for complex search requirements.
2Measurement precision
If the system generates images using deep learning models like GANs, then the accuracy and semantic relevance of search results improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary image retrieval to identify relevant base images before initiating the generation process. By pre-selecting appropriate source images and detecting missing components in advance, the system reduces the computational burden and processing time required for image generation while maintaining high semantic accuracy.
Solution Approach 2:
The system generates only the specific missing components identified through detection rather than creating entire images from scratch. This partial generation approach significantly reduces processing time and computational resources while still achieving high semantic accuracy by focusing generation efforts only where needed.
Data Source
AI summary
The present disclosure provides method and apparatus for generative image acquisition. A query can be received. A first set of images retrieved according to the query can be obtained. It is can be determined that the first set of images includes a first image that partially satisfies the query. A missing component of the first image compared to the query can be detected. A second set of images based on the first image and the missing component can be generated.