Vision-Language Transformer Pre-Training With Misalignment Distillation
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Solution Overview
Problem
Existing vision-language transformer pre-training methods face challenges with excessive data processing overhead and misalignment issues due to image augmentation, leading to deteriorated performance.
Innovation Solution
A method that intentionally induces misalignment between augmented images and text by random enlargement or masking, utilizing misalignment as useful information through a contrast image-text pre-training method (MCD) with knowledge distillation, aligning feature vector representations using alignment matrices and momentum-based encoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image augmentation (random enlargement, cropping, masking) is applied to secure large-scale pre-training data, then the quantity of pre-training data increases, but misalignment between augmented images and text occurs leading to deteriorated model performance
Solution Approach 1:
The patent converts the harmful misalignment effect into a beneficial learning signal by introducing a mask token that represents the augmented (misaligned) region. The model learns to predict this mask token, thereby learning to handle misalignment rather than being degraded by it. This transforms the data quality issue into a useful training objective that improves model robustness.
Solution Approach 2:
The mask token acts as an intermediary element between the augmented image and the original text. Instead of directly comparing misaligned image regions with text (which causes performance degradation), the mask token mediates this relationship by providing a structured representation of the augmentation operation, allowing the model to learn appropriate associations.
2Adaptability or versatility
If conventional image augmentation is used to expand dataset size, then data diversity increases, but data processing overhead becomes excessive
Solution Approach 1:
The patent performs preliminary recording of augmentation operations (translation vectors, mask parameters) during data generation. This pre-recording of transformation metadata allows the model to efficiently process augmented data without requiring re-computation or complex tracking of augmentation steps during training, thereby reducing processing overhead while maintaining data diversity.
3Measurement precision
If external modules (object detector, summary extractor) are introduced to correct misalignment, then alignment accuracy improves, but device complexity and processing burden increase
Solution Approach 1:
The patent enables the vision-language transformer to handle misalignment internally through the mask token mechanism and augmented image processing, without requiring external correction modules. The model learns to self-correct for misalignment by predicting mask tokens and processing augmented images directly, eliminating the need for separate object detectors or summary extractors.
Data Source
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AI summary
The present disclosure relates to a method and a system for promptly training a simplified vision-language transformer, in which large uncurated datasets are augmented through image enlargement and/or masking, etc. and vision-language transformers are pre-trained by reflecting, through a knowledge distillation framework, misaligned information between an augmented image and text upon the augmentation, thereby reducing data processing overhead.