Multimodal Feature Alignment for Fast Product Image Retrieval
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Solution Overview
Problem
Existing product image retrieval systems face challenges in precision due to similar product appearances and varying user photography angles, leading to low retrieval accuracy and high storage requirements, while also suffering from slow retrieval speeds in large-scale systems.
Innovation Solution
A model training method that integrates image and text features using multiple loss values to align and differentiate image representations, combined with hash feature optimization for efficient storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple loss values are used to align image and text features, then image retrieval precision is improved, but model training complexity increases
Solution Approach 1:
The patent segments the feature alignment process into multiple independent loss functions (contrastive loss, triplet loss, classification loss), each targeting specific aspect of feature alignment. This segmentation allows complex multi-objective optimization to be broken down into manageable components that can be computed and optimized separately while contributing to the overall retrieval precision.
Solution Approach 2:
The patent employs a unified model framework that simultaneously performs multiple functions: feature extraction, feature alignment through multiple loss functions, and retrieval. This multi-functional approach allows the system to achieve high retrieval precision while avoiding the need for separate specialized modules for each function, thereby managing complexity.
2Measurement precision
If image and text features are aligned in homogeneous space, then retrieval accuracy improves, but computational resources and storage requirements increase
Solution Approach 1:
The patent merges image features and text features into a single homogeneous feature space through contrastive loss optimization. By projecting both modalities into the same vector space, the system enables direct similarity comparison between image-query pairs, improving retrieval accuracy while storing only the essential feature vectors rather than redundant multi-modal data.
Solution Approach 2:
The patent transforms heterogeneous image and text data into standardized vector representations with consistent dimensions and scales. This parameter transformation allows features from different modalities to be stored and compared uniformly, reducing storage requirements while maintaining the discriminative power needed for accurate retrieval.
3Productivity
If hash feature optimization is implemented, then retrieval speed improves, but retrieval precision may be reduced
Solution Approach 1:
The patent performs preliminary feature alignment and optimization before the actual retrieval process by pre-training the model with multiple loss functions to establish optimal feature representations. This preliminary action ensures that when retrieval is executed, the pre-aligned features enable fast hashing and matching while maintaining high precision, as the feature space has already been optimized for both speed and accuracy.
4Measurement precision
If different image feature extraction models are used for different images, then feature representation capability improves, but system complexity and processing time increase
Solution Approach 1:
The patent employs a universal image feature extraction model that has been trained to handle diverse image types and conditions through multi-task learning with contrastive, triplet, and classification losses. This single multi-functional model replaces the need for multiple specialized models, maintaining strong feature representation capability across different product images while significantly reducing processing time and system complexity.
Data Source
AI summary
A model training method relates to the artificial intelligence field. The method includes: obtaining a plurality of training samples, where each training sample includes an image and a text, and the text describes a target object in the image; and inputting the plurality of training samples into a target model, so that the target model performs the following procedure until a preset stop condition is met: extracting an image feature of a first image and a text feature of a first text; obtaining a first loss value based on a difference between a first vector and a second vector, where a dimension of the first vector is the same as a dimension of the second vector; and updating the target model based on the first loss value.