Multimodal Feature Extraction via Asynchronous Cross-Modal Encoding
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Solution Overview
Problem
Current methods for image-text matching in vision and language tasks face challenges in aligning semantics between different modalities, particularly due to the disparity in information density between image and text data, leading to inefficient feature extraction and poor performance in cross-modal alignment.
Innovation Solution
A feature extraction model with alternately deployed cross-modal and visual encoding parts is used to perform asynchronous cross-modal semantic alignment, densely encoding image data while sparingly encoding text data, thereby capturing the asynchronous semantic alignment of image and text modalities effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dense cross-modal encoding is applied to both image and text data, then cross-modal alignment is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The encoding process is segmented into two distinct stages: a cross-modal encoding stage that processes both image and text data together to establish alignment, and a separate visual encoding stage that processes only image data. This segmentation allows the model to achieve cross-modal alignment without applying dense encoding to both modalities simultaneously, thereby reducing computational complexity while maintaining alignment accuracy.
Solution Approach 2:
The patent applies partial encoding by performing cross-modal encoding only on the necessary portions of the data (image-text pairs) rather than densely encoding all data. The visual encoding part processes only the image modality after cross-modal alignment is established, avoiding redundant computation on text data that does not require visual processing, thus reducing overall computational burden while maintaining sufficient encoding depth for alignment tasks.
2Adaptability or versatility
If asynchronous encoding is used for image and text data, then information density differences are addressed, but model architecture complexity increases
Solution Approach 1:
The model architecture is segmented into functionally distinct parts: a cross-modal encoding part that handles both image and text data for alignment, and a visual encoding part that handles only image data. This functional segmentation allows asynchronous processing where image and text data can be encoded at different rates and depths according to their information density requirements, while keeping the architecture manageable through clear modular separation.
Solution Approach 2:
Different parts of the model apply different encoding densities to different modalities based on their specific requirements. The cross-modal encoding part applies unified encoding to both modalities for alignment, while the visual encoding part applies density-appropriate encoding to image data. This local quality differentiation allows the model to handle information density disparities effectively without requiring uniform dense encoding across all data types.
3Reliability
If cross-modal encoding is performed on all intermediate features, then semantic alignment is enhanced, but processing time increases
Solution Approach 1:
The encoding process is segmented into a cross-modal encoding stage that performs semantic alignment on intermediate features, and a subsequent visual encoding stage that processes only the image modality. This segmentation allows the model to perform cross-modal alignment efficiently on necessary intermediate features without repeatedly processing all features through both encoding paths, thereby reducing processing time while maintaining semantic alignment quality.
Solution Approach 2:
The cross-modal encoding part performs preliminary alignment of image and text features at intermediate stages before the final visual encoding. This preliminary action establishes the semantic alignment foundation early in the processing pipeline, avoiding the need to re-process all features through complete cross-modal encoding at every stage, thus reducing overall processing time while maintaining alignment effectiveness.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for multimodal data processing. A method comprises: obtaining image data and text data; and extracting a target visual feature of image data and a target textual feature of text data using a feature extraction model. The feature extraction model comprises alternatively deployed cross-modal encoding parts and visual encoding parts. The extracting comprises: performing, using a first cross-modal encoding part of the feature extraction model, cross-modal feature encoding on a first intermediate visual feature of the image data and a first intermediate textual feature of the text data, to obtain a second intermediate visual feature and a second intermediate textual feature; performing, using a first visual encoding part of the feature extraction model, visual modal feature encoding on the second intermediate visual feature, to obtain a third intermediate visual feature.