Multimodal Representation Learning via Self-Attention Encoder
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Solution Overview
Problem
Existing multimodal representation learning methods, such as R-CNN, are heavily dependent on object detection and require additional labels for training, making them domain-specific and inefficient for analyzing multimodal data.
Innovation Solution
An apparatus and method that combines an image processor, a text processor, and an encoder to generate multimodal representation vectors through self-attention, using convolutional neural networks and loss functions like ITM, MLM, and MAM to analyze and match image and text data, allowing for domain-agnostic feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If R-CNN is used for object detection and feature extraction, then feature extraction capability is improved, but domain specificity increases and additional labels are required
Solution Approach 1:
The patent applies universality by creating a domain-agnostic multimodal representation learning framework that can process both image and text data through a unified encoder. The encoder generates multimodal representation vectors that work across different domains without requiring domain-specific R-CNN models, thereby achieving both feature extraction capability and cross-domain adaptability.
Solution Approach 2:
The patent uses an intermediary approach by introducing a shared encoder that acts as a mediator between image and text processing. This encoder receives concatenated embedding vectors from both modalities and generates unified multimodal representation vectors, eliminating the need for separate domain-specific models and additional detection labels.
2Reliability
If R-CNN is trained for each domain, then domain-specific performance is improved, but training complexity and data requirements increase
Solution Approach 1:
The patent eliminates the need for separate domain-specific models by implementing a universal encoder that handles both image and text processing. This single encoder can be trained on multimodal data from different domains simultaneously, reducing training complexity while maintaining performance across domains through the shared representation learning capability.
3Measurement precision
If additional labels are required for training, then object detection accuracy is improved, but data preparation complexity increases
Solution Approach 1:
The patent extracts the essential information needed for multimodal representation learning from the input data itself without requiring additional labels. The encoder processes concatenated embedding vectors from image and text modalities to generate representation vectors, eliminating the need for separate detection labels and simplifying data preparation while maintaining representation accuracy.
Data Source
AI summary
An apparatus for analyzing multimodal data includes an image processor configured to generate an activation embedding vector based on an index of an activation map obtained from image data through a convolutional neutral network, a text processor configured to receive text data to generate a text embedding vector, a vector concatenator configured to concatenate the activation embedding vector and the text embedding vector to each other to generate a concatenated embedding vector, and an encoder configured to generate a multimodal representation vector in consideration of an influence between elements constituting the concatenated embedding vector based on self-attention.