Adaptive Modality Weighting for Product Attribute Prediction
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Solution Overview
Problem
Existing methods for predicting product attributes in e-commerce using multimodal deep learning, such as combining image and text data, are not accurate for all products due to varying information modalities, leading to inconsistent attribute identification.
Innovation Solution
An information processing apparatus and method that acquires multiple modalities associated with an object, generates feature values, derives weights for each modality based on feature values and object information, and predicts attributes by concatenating weighted feature values, using attention weights to indicate the importance of each modality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If multiple modalities are combined using simple concatenation for all products, then the implementation is simple and consistent, but the attribute identification accuracy deteriorates due to varying information modalities across different products
Solution Approach 1:
The patent applies dynamics by making the modality combination method adaptive rather than static. The system dynamically selects between concatenation and attention mechanism based on the characteristics of each product's modalities, allowing the processing approach to change according to the specific input data rather than using a fixed method for all products
Solution Approach 2:
The patent changes the parameter of modality combination from a fixed simple concatenation to a flexible approach that can switch between concatenation and attention mechanisms. This parameter change allows the system to adjust the weighting and integration method of different modalities based on product-specific characteristics, improving accuracy without significantly increasing implementation complexity
2Measurement precision
If different information modalities are used for different products, then the attribute identification accuracy improves, but the system complexity increases due to varying processing requirements
Solution Approach 1:
The patent applies universality by creating a multi-functional system that can handle both simple concatenation and attention mechanisms within a single framework. The system is designed to accommodate different modality combinations and processing methods universally, allowing it to adapt to various product types without requiring separate specialized processing pipelines for each case
3Measurement precision
If attention weights are derived for each modality based on feature values, then the prediction accuracy improves by indicating modality importance, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by implementing attention mechanisms selectively rather than universally. The system uses attention weights when they provide significant benefit for specific product types or modality combinations, while relying on simpler concatenation for cases where it suffices. This partial application of the more complex attention mechanism reduces overall computational burden while maintaining accuracy improvements where needed
Data Source
AI summary
An information processing apparatus (1) comprises: an acquisition unit (11) configured to acquire a plurality of modalities associated with an object and information identifying the object; a feature generation unit (12) configured to generate feature values for each of the plurality of modalities; a deriving unit (13) configured to derive weights corresponding to each of the plurality of modalities based on the feature values for each of the plurality of modalities and information identifying the object; and a prediction unit (14) configured to predict an attribute of the object from a concatenated value of the feature values for each of the plurality of modalities, weighted by the corresponding weights.