Multi-Modal Spoken Query Image Search System
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Solution Overview
Problem
Conventional image search systems face challenges in efficiently determining the relevance of images to spoken queries, leading to increased computing resource consumption and user frustration due to repetitive search queries and inadequate search results.
Innovation Solution
An image search system utilizing a multi-modal model that combines a spoken language model and an image processing model to generate relevance scores for images based on spoken queries, trained using a curriculum approach to improve data efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image search systems are used, then search queries can be processed, but computing resource consumption increases and search accuracy decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing spoken queries into text representations and pre-computing relevance scores using the multi-modal model before actual search execution. This preparation work reduces the computational burden during real-time search operations, lowering computing resource consumption while maintaining high search accuracy.
Solution Approach 2:
The patent introduces a text representation as an intermediary between the spoken query and the image search process. The spoken query is converted to text, which then serves as the basis for relevance scoring against image metadata. This intermediary step enables more efficient processing compared to direct speech-to-image matching, reducing computational resources while improving reliability.
2Ease of operation
If conventional image search systems are used, then basic search functionality is provided, but user frustration increases due to repetitive queries and inadequate results
Solution Approach 1:
The system implements feedback mechanisms by analyzing user interactions with search results and using this information to refine future search operations. The multi-modal model continuously learns from user behavior patterns, improving the accuracy of relevance scoring over time. This reduces the need for users to submit repetitive queries and enhances overall ease of operation.
Solution Approach 2:
The system performs preliminary analysis of user intent through spoken query processing and text representation before executing the search. By understanding user intent in advance and pre-computing relevant results, the system reduces the time users spend on repetitive search attempts and improves the quality of results returned on the first query.
3Measurement precision
If multi-modal model with curriculum training is used, then search accuracy and data efficiency improve, but model training complexity increases
Solution Approach 1:
The patent segments the model training process into distinct curriculum stages, where different components (spoken language model, text representation model, image processing model) are trained separately in sequence. This segmentation allows each component to be optimized independently with appropriate training data and techniques, improving overall measurement precision while managing training complexity through structured decomposition.
Solution Approach 2:
The curriculum training approach performs preliminary training of individual model components before integrating them into the full multi-modal system. Each component is pre-trained on its specific task (speech recognition, text processing, image analysis) before joint training. This preliminary action simplifies the overall training process by breaking it into manageable stages while achieving high relevance score accuracy.
Data Source
AI summary
An image search system uses a multi-modal model to determine relevance of images to a spoken query. The multi-modal model includes a spoken language model that extracts features from spoken query and a language processing model that extract features from an image. The multi-model model determines a relevance score for the image and the spoken query based on the extracted features. The multi-modal model is trained using a curriculum approach that includes training the spoken language model using audio data. Subsequently, a training dataset comprising a plurality of spoken queries and one or more images associated with each spoken query is used to jointly train the spoken language model and an image processing model to provide a trained multi-modal model.