Generative Inpainting for Accurate Visual Item Retrieval
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Solution Overview
Problem
Existing search technologies face inefficiencies in locating relevant items due to limited query understanding, leading to increased computing resource consumption and repetitive user inputs.
Innovation Solution
A search system that utilizes generative inpainting, allowing users to select an input image, apply a mask, and generate an inpainted image using a generative model, which is then used to query an item data store for relevant items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search technologies are used to locate relevant items, then search queries can be processed, but computing resource consumption increases and query understanding remains limited
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate queries before final execution. The query generation component creates several potential queries based on the user input, and the rephrasing component prepares alternative formulations in advance. This preliminary processing allows the system to select the most effective query, reducing the need for repeated search attempts and thereby lowering overall computing resource consumption while improving query understanding accuracy.
2Measurement precision
If users provide repetitive inputs to improve search accuracy, then relevant items can be located, but user effort and time increase
Solution Approach 1:
The system implements self-service by automatically generating and rephrasing multiple candidate queries without requiring user intervention. The query generation component autonomously creates alternative queries, and the rephrasing component automatically reformulates them. This eliminates the need for users to manually provide repetitive inputs, reducing user effort and time loss while maintaining high search relevance accuracy through the system's autonomous query optimization.
3Reliability
If multiple query attempts are made to improve item retrieval, then relevant items can be found, but storage device I/O efficiency decreases
Solution Approach 1:
The system performs preliminary query generation and rephrasing before executing the search query against the storage device. By preparing multiple candidate queries in advance and selecting the most promising ones, the system reduces the number of actual storage device I/O operations needed. This preliminary processing improves item retrieval accuracy while enhancing query execution efficiency by minimizing redundant storage access operations.
Data Source
AI summary
Aspects of the technology described herein relate to performing item retrieval using generative inpainting. In accordance with some aspects, an image having one or more objects is accessed. Responsive to user input, a mask is applied to the image to provide a masked image, where the mask overlays at least a portion of a first object from the one or more objects. A generative model generates an inpainted image by inpainting the mask of the masked image. One or more search results are identified using the inpainted image and are provided for presentation.