Vision-Language Transformer Pre-Training With Misaligned Image-Text Pairs
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Solution Overview
Problem
Existing vision-language transformer pre-training methods require large-scale data sets and incur excessive data processing overhead due to misalignment issues during image augmentation, leading to disappointing performance and resource burden when using external modules for correction.
Innovation Solution
A method that intentionally induces misalignment between augmented images and text by random enlargement or masking, utilizing misaligned image-text pairs for pre-training through a contrast image-text pre-training method (MCD) with knowledge distillation to enhance efficiency and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image augmentation is performed during pre-training, then data diversity is improved, but text-image alignment deteriorates
Solution Approach 1:
The patent converts the harmful misalignment effect into a beneficial learning signal by introducing a dual-branch architecture: one branch learns from aligned image-text pairs while the other learns from misaligned pairs. The contrastive learning objective uses the misaligned pairs as negative samples, transforming the alignment problem into a discrimination task that improves robustness while maintaining diversity.
Solution Approach 2:
The patent segments the pre-training process into two parallel learning pathways: (1) aligned image-text pairing for learning semantic correspondence, and (2) misaligned pairing for learning invariance to augmentation. This segmentation allows the model to simultaneously handle both alignment and diversity requirements without conflict.
2Manufacturing precision
If external modules are used to correct misalignment, then alignment accuracy is improved, but system complexity and resource burden increase
Solution Approach 1:
The patent extracts the alignment correction function from external modules and integrates it directly into the transformer architecture through attention mechanisms. The self-attention and cross-attention layers inherently perform alignment by learning to attend to relevant image regions corresponding to text tokens, eliminating the need for separate correction modules.
Solution Approach 2:
The patent merges the alignment learning task with the main pre-training objective by unified contrastive loss function that simultaneously optimizes both aligned and misaligned pairs. This integration allows the model to learn alignment and robustness in a single end-to-end training process without requiring separate correction systems.
3Reliability
If large-scale data sets are used for pre-training, then model performance is improved, but data processing overhead increases
Solution Approach 1:
The patent applies partial augmentation strategies by selectively applying different augmentation levels to different branches: mild augmentation for the aligned branch and strong augmentation for the misaligned branch. This partial application of augmentation reduces overall processing complexity while maintaining the benefits of data diversity for model performance.
Data Source
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 (e.g., 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 both the necessary size of the utilized data set and data processing overhead.