Multimodal Image Processing With Cross-Modal Semantic Alignment
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Solution Overview
Problem
Existing image processing methods using transformer-based multimodal large-scale models struggle to achieve effective semantic understanding in complex tasks such as image understanding and interlaced image-text data generation, particularly in forms and document understanding, due to separate encoders for image generation and understanding, or insufficient semantic perception capabilities of CNN image encoders.
Innovation Solution
A method involving cross-modal semantic modeling to integrate multimodal semantic information by employing self-attention or cross-attention mechanisms to align and fuse text and image token sequences, generating a joint feature representation that captures semantic correlations across modalities, enhancing contextual understanding and cross-modal reasoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate encoders are used for image generation and understanding, then the system can handle different tasks independently, but the semantic understanding capability in complex tasks deteriorates
Solution Approach 1:
The patent merges the image generation encoder and image understanding encoder into a unified multimodal encoder that processes both text and image inputs simultaneously. This unified encoder uses cross-attention mechanisms to capture semantic correlations between different modalities, resolving the contradiction by combining previously separate functions into an integrated system that maintains both task independence and enhanced semantic understanding through joint feature representation.
2Speed
If CNN image encoders are used, then the processing speed is fast, but the semantic perception capability is insufficient
Solution Approach 1:
The patent transforms the image encoder from a traditional CNN architecture to a transformer-based architecture that processes image tokens sequentially with self-attention mechanisms. This parameter change in the encoder architecture enables the model to capture long-range dependencies and semantic relationships in images, significantly improving semantic perception capability while maintaining efficient processing through parallel computation capabilities of the transformer.
3Measurement precision
If cross-modal semantic modeling is performed to integrate multimodal semantic information, then the image understanding capability is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the complex cross-modal processing into distinct modular components: text tokenization, image tokenization, separate encoding pathways for text and images, cross-attention layers for integration, and a unified decoding mechanism. This segmentation allows the system to manage computational complexity through organized modularity while still achieving comprehensive cross-modal semantic understanding through the coordinated interaction of these specialized components.
Data Source
AI summary
The disclosure provides a method and an apparatus for processing an image, an electronic device, and a storage medium, which relates to the field of artificial intelligence technologies, and particularly to a technical field such as computer vision, deep learning, and large-scale models. The solution includes: obtaining an input content adapted to an image processing task, in which the input content includes at least one of: a first text token sequence, a first image token sequence, or an image-text fusion sequence; obtaining a joint feature representation including multimodal semantic information by performing cross-modal semantic modeling on the input content, in which the multimodal semantic information indicates a semantic correlation relationship of the input content in different modalities; and generating an output content adapted to the image processing task based on the joint feature representation.