Unified Embedding Space for Multi-Modal Image Search and Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, relying on user-provided reference images and requiring significant user interaction for modifications, while also being limited in their search capabilities and control over multi-modal search inputs.
Innovation Solution
A search-based editing system that utilizes multiple large-scale search engines to retrieve digital images in response to various search queries, including textual-visual searches and sketch searches, and applies powerful image-editing techniques like color transfer, tone transfer, and texture transfer to modify input images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems use user-provided reference images, then users have control over the reference images, but the system becomes inflexible and requires significant user interaction
Solution Approach 1:
The system automatically retrieves reference images from a large-scale visual corpus using search engines based on the input image and user preferences, eliminating the need for users to manually provide reference images. The system serves itself by autonomously performing image search, selection, and retrieval operations that would otherwise require significant user interaction.
Solution Approach 2:
The patent introduces search engines as intermediaries between the user and the reference image database. Instead of users directly browsing and selecting reference images, the search engines mediate by automatically querying the visual corpus based on embeddings of the input image and user preferences, returning relevant reference images for editing.
2Measurement precision
If the system uses multiple large-scale search engines for image retrieval, then search accuracy and versatility improve, but system complexity increases
Solution Approach 1:
The patent employs multiple search engines that can handle different types of search queries (textual-visual searches, sketch searches) within a unified framework. These search engines are integrated into the existing image editing platform, allowing them to serve multiple functions: retrieving reference images, finding similar images, and supporting various editing operations through a common embedding space architecture.
Solution Approach 2:
The patent merges multiple search engines and their respective functionalities into a unified search system. Different search engines are combined to handle diverse query types (text-based, image-based, sketch-based) and are integrated with the image editing workflow, allowing seamless operation within a single application framework rather than requiring separate tools.
3Adaptability or versatility
If the system bridges search and editing processes, then flexibility and control over multi-modal search inputs improve, but the system complexity increases
Solution Approach 1:
The patent creates a universal embedding space that can represent and process multiple types of input modalities (text queries, image queries, sketch queries) uniformly. This multi-functional embedding space allows the system to handle different search query types through the same architectural framework, enabling flexible control over multi-modal search inputs while maintaining system coherence.
Solution Approach 2:
The patent introduces a common embedding space as an additional dimensional representation that unifies different input modalities. By transforming text, image, and sketch inputs into a shared embedding dimension, the system achieves flexible multi-modal search control while managing complexity through this unified representation space rather than requiring separate processing pipelines for each modality.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, one or more embodiments involve receiving an input digital image and search input and further modifying the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and one or more embodiments involve retrieving the image search results utilizing a weighted combination of the queries. Some implementations involve generating an input embedding for the search input (e.g., the multi-modal search input) and retrieving the image search results using the input embedding.