Cross-Modal Contrastive Learning for Item Categorization
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Solution Overview
Problem
Current contrastive learning (CL) methods face challenges in efficiently leveraging cross-modal signals between text and images for item categorization (IC) in e-commerce, leading to resource-demanding dual-input multimodal systems that hinder the development of high-performance IC models.
Innovation Solution
The proposed solution involves using an image-based transformer and a modified bidirectional encoder representation from transformers (BERT) model for self-supervised learning, where text and image representations are generated and paired to improve IC performance without requiring costly dual-input systems, by leveraging cross-modal signals in a self-supervised manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual-input multimodal systems are used to process both text and images for item categorization, then prediction accuracy is improved, but computational resources and system complexity increase significantly
Solution Approach 1:
The system segments the processing task into two distinct phases: a training phase where image data is processed to enhance text representations, and an inference phase where only text processing is required. This segmentation allows the system to leverage image information during training without requiring dual-input capability during deployment, thereby reducing operational complexity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary processing of image data during the training phase to pre-compute enhanced text representations. By preparing and storing these enhanced representations beforehand, the system eliminates the need for real-time image processing during inference, reducing computational complexity at deployment while preserving the accuracy benefits of multimodal processing.
2Measurement precision
If dual-input multimodal systems are used to process both text and images for item categorization, then prediction accuracy is improved, but computational costs increase
Solution Approach 1:
The system performs image processing and representation enhancement during the training phase as a preliminary action. By completing these computationally intensive tasks beforehand and storing the results, the system avoids repeated expensive image processing during inference, significantly reducing operational computational costs while maintaining the accuracy improvements from multimodal processing.
Solution Approach 2:
The system creates a copy of the enhanced text representations that were derived from image processing during training. These copied representations can be directly used during inference without re-processing the images, eliminating the need for repeated computationally expensive image analysis and reducing operational computational costs.
3Measurement precision
If contrastive learning is applied to leverage cross-modal signals between text and images, then item categorization performance is improved, but resource requirements increase
Solution Approach 1:
The system segments the resource consumption into training and inference phases. During training, substantial computational resources are consumed to process images and compute contrastive losses for enhancing text representations. During inference, the system uses the pre-computed enhanced representations, requiring minimal additional resources, thus maintaining high categorization performance while reducing operational resource requirements.
Data Source
AI summary
A method of contrastive learning (CL) performed by at least one processor includes receiving, by at least one processor implementing a text-based transformer, a text title corresponding to an anchor, generating, by the at least one processor implementing the text-based transformer, a text representation of the text title, receiving, by at least one processor implementing an image-based transformer, a first image corresponding to the anchor, generating, by the at least one processor implementing the image-based transformer, a first image representation of the first image corresponding to the anchor, and constructing a positive pair between the text representation and the first image representation.