Multi-Modal Document Training Using CLIP-MIL Text Bags
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Solution Overview
Problem
Existing multi-modal models struggle to effectively utilize specialized data, such as illustrated technical or scientific documentation, due to domain gaps and limited representation of under-represented features, leading to inadequate performance in expert applications.
Innovation Solution
A self-supervised system combining CLIP-MIL and CLIP-LoRA is used to automatically extract text and images from documents, generating text bags and training multi-modal models with a CLIP-MIL loss, adapting LoRA for neural networks to improve representation of expert data using multiple instance learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing multi-modal models are used for expert applications, then general performance is maintained, but performance on specialized data (technical/scientific documentation) deteriorates due to domain gaps and limited representation of under-represented features
Solution Approach 1:
The system performs preliminary extraction of text and images from documents before training, organizing data into text bags that are then used to fine-tune the multi-modal model. This preliminary preparation of specialized data enables the model to better represent expert domain features before actual training occurs
Solution Approach 2:
The system fine-tunes the multi-modal model by adjusting its parameters using specialized expert data. The CLIP-MIL loss function computes correlations between images and texts in a CLIP feature space, enabling parameter optimization that improves representation of under-represented features in technical andscientific documentation
2Reliability
If multi-modal models are trained on specialized data, then representation of under-represented features improves, but training complexity and computational resources increase
Solution Approach 1:
The system uses a self-supervised approach where the multi-modal model automatically extracts text and images from documents and generates text bags without requiring manual annotation. This self-service mechanism reduces the need for complex human-in-the-loop training pipelines while improving representation of expert features
Solution Approach 2:
The system introduces text bags as an intermediary data structure that bridges raw documents and the multi-modal model training process. These text bags organize extracted text and image relationships, simplifying the training input format and reducing computational complexity compared to processing raw documents directly
3Measurement precision
If manual annotation of training data is performed, then training data quality improves, but time consumption and labor costs increase significantly
Solution Approach 1:
The system automatically extracts text and images from documents using the multi-modal model itself, generating training data without human intervention. This self-service annotation process maintains sufficient data quality for training while eliminating the time-consuming manual annotation step entirely
Solution Approach 2:
The system creates text bags that are copies or representations of the relationships between text and images in the original documents. These copied structures capture the essential information needed for training without requiring manual verification or annotation of each individual relationship
Data Source
AI summary
An example system includes a processor to automatically extract text and images from a document. The processor can automatically generate text bags including a number of nearest texts for each of the extracted images. The processor can then train a multi-modal model based on the automatically generated text bags using a CLIP-MIL loss that computes, for each of the extracted images, a correlation between each of the different texts in the texts bags using a CLIP feature space at each gradient step of the gradient descent-based multiple instance learning (MIL) algorithm.