Cross-Modal Contrastive Learning for Complex Text-to-Image Generation
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Solution Overview
Problem
Existing text-to-image synthesis models struggle with generating high-quality images from complex scenes with multiple objects, requiring fine-grained object labels and being cumbersome for real-world scenarios, and lack effective methods for aligning textual descriptions with generated images.
Innovation Solution
A Cross-Modal Contrastive Generative Adversarial Network (XMC-GAN) that employs multiple contrastive losses to optimize mutual information between text and images, using an attentional self-modulation generator and a contrastive discriminator for end-to-end training without object-level annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If hierarchical models with fine-grained object labels are used, then generation quality for complex scenes is improved, but device complexity and ease of operation deteriorate due to multi-step processing requirements
Solution Approach 1:
The patent extracts the fine-grained object label requirement from the training process by using contrastive learning with image-to-image and text-to-image pairs. This allows the model to learn alignment between text descriptions and image regions without requiring explicit object-level annotations during training, thereby reducing the complexity of data preparation and model architecture while maintaining generation quality.
Solution Approach 2:
The patent introduces contrastive loss as an intermediary mechanism that mediates between text descriptions and image generation. By formulating the problem as a contrastive learning task with positive pairs (image-to-image, text-to-image) and negative pairs, the model can achieve fine-grained alignment without requiring hierarchical object labels or multi-step processing, thus simplifying the overall system.
2Measurement precision
If contrastive learning with multiple loss functions is applied, then alignment between text and image is improved, but training complexity increases
Solution Approach 1:
The patent merges multiple contrastive loss functions (image-to-image contrastive loss and text-to-image contrastive loss) into a unified training framework. By combining these losses and using a single generator-discriminator architecture with shared parameters, the model achieves comprehensive text-image alignment without requiring separate training procedures or complex multi-stage optimization, thus managing training complexity while improving alignment precision.
Data Source
AI summary
A computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene.