Cross-Modality Transformer Pre-training for Fine-Grained Fashion Representation
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Solution Overview
Problem
Current vision-linguistic models are sub-optimal for fine-grained representation learning in domain-specific tasks like fashion, as they primarily focus on coarse representations, making them unfavorable for attribute-aware tasks such as searching for specific fashion catalog objects.
Innovation Solution
A cross-modality transformer-based model is pre-trained using a multi-modal approach that aligns and masks image patches and text tokens to learn fine-grained representations, employing a 'kaleidoscope' patch strategy and attention-based alignment to bridge the semantic gap between text and image modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general VL models (UNITER, VL-BERT) are used for domain-specific tasks, then the model architecture remains simple and general-purpose, but the representation learning capability deteriorates for fine-grained domain-specific features
Solution Approach 1:
The patent segments the image into multiple patches and processes them through separate visual embedding layers, allowing fine-grained regional analysis. The text is segmented into tokens processed through language embedding layers. This segmentation enables the model to capture detailed local features while maintaining overall contextual understanding, resolving the contradiction between fine-grained capability and model complexity.
Solution Approach 2:
The patent introduces a new dimension by adding domain-specific embedding layers for visual and language modalities separately before fusion. This dimensional expansion allows the model to learn domain-specific representations without fundamentally changing the core transformer architecture, thereby improving adaptability while controlling complexity.
2Measurement precision
If fine-grained representation learning is implemented through domain-specific embedding layers, then the learning accuracy for domain-specific tasks improves, but the training data requirements and computational resources increase
Solution Approach 1:
The patent applies preliminary domain-specific pre-training to the embedding layers before fine-tuning on specific tasks. This preliminary action allows the model to learn general domain-specific representations from large-scale data first, then adapt to specific tasks with less data, reducing the overall training data requirement while maintaining high accuracy.
Solution Approach 2:
The patent applies different embedding strategies to different parts of the model: domain-specific embedding layers for visual and language inputs, and general transformer layers for processing. This localized application of complexity allows high accuracy in attribute-aware recognition while managing training requirements through selective specialization.
Data Source
AI summary
A transformer based vision-linguistic (VL) model and training technique uses a number of different image patches covering the same portion of an image, along with a text description of the image to train the model. The model and pre-training techniques may be used in domain specific training of the model. The model can be used for fine-grained image-text tasks in the fashion domain.