Joint Visual-Semantic Embedding for Image Search Accuracy
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Solution Overview
Problem
Conventional image search systems face inaccuracies and inefficiencies in generating search results due to limitations in conveying accurate and coherent information, often relying on separate models for visual and textual matching, which can lead to loss of coherence and accuracy in search results.
Innovation Solution
A joint visual-semantic embedding and grounding model is trained using multi-task learning to generate search results by encoding images and textual information into feature vectors, creating image-text pairs, and generating visual-semantic embeddings based on spatial and semantic information, allowing for fine-grained localization and relevance ranking of image results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate models are used for visual and textual matching, then model complexity is reduced and ease of manufacture is improved, but accuracy and coherence of search results deteriorate
Solution Approach 1:
The patent merges separate visual and textual matching models into a single joint visual-semantic embedding model. This unified model simultaneously processes visual features and textual information, creating joint embeddings that capture both modalities in a shared vector space, thereby improving search result accuracy and coherence while maintaining manageable model complexity through integrated architecture
2Manufacturing precision
If joint visual-semantic embedding model is used, then search result accuracy is improved, but device complexity increases
Solution Approach 1:
The joint visual-semantic embedding model serves multiple functions simultaneously: it performs visual feature extraction, textual encoding, cross-modal matching, and semantic alignment all within a single unified architecture. This multi-functionality reduces the need for multiple separate components, managing overall system complexity while achieving high search accuracy through integrated processing
Solution Approach 2:
The model employs parameter sharing and joint optimization strategies where visual and textual parameters are learned simultaneously in a shared embedding space. By changing the parameter representation to use common embedding dimensions for both modalities, the model achieves high accuracy without proportionally increasing complexity, as the same parameter sets serve dual purposes
3Productivity
If separate visual and textual matching systems are used, then processing efficiency is maintained, but coherence and relevance of search results deteriorate
Solution Approach 1:
By combining visual and textual processing into a single joint embedding model, the system achieves coherent search results through unified semantic understanding. The model processes both modalities simultaneously in a shared vector space, ensuring that visual and textual information are aligned and consistent, thereby improving result coherence while maintaining efficient single-pass processing
Data Source
AI summary
Certain embodiments involve a method for generating a search result. The method includes processing devices performing operations including receiving a query having a text input by a joint embedding model trained to generate an image result. Training the joint embedding model includes accessing a set of images and textual information. Training further includes encoding the images into image feature vectors based on spatial features. Further, training includes encoding the textual information into textual feature vectors based on semantic information. Training further includes generating a set of image-text pairs based on matches between image feature vectors and textual feature vectors. Further, training includes generating a visual grounding dataset based on spatial information. Training further includes generating a set of visual-semantic joint embeddings by grounding the image-text pairs with the visual grounding dataset. Additionally, operations include generating an image result for display by the joint embedding model based on the text input.