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AI Research
arXiv CS.AI·Jun 10·5 min read
Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding
TL;DR
Existing methods for Foreground Conditioned Out-Painting (FCO) often produce artifacts that reduce image quality by misaligning text prompts with visual instances. The Customized Concept Embedding Diffusion (CCE-Diffusion) framework was developed to enhance text-instance alignment by customizing concept embeddings.
✦ Why It Matters
Engineers can implement CCE-Diffusion to improve image generation quality in applications requiring background customization.
Key Takeaways
- Utilize CCE-Diffusion to enhance image generation quality.
- Integrate CCE-Module with existing FCO methods for better results.
- Apply Instance-Aware Loss for optimizing concept embeddings.
- Ensure prompt integrity with Semantic-Preserving Prompt Template.
Full Summary
Merchants face high costs in creating quality display images, and Foreground Conditioned Out-Painting (FCO) offers a cost-effective solution by generating backgrounds based on text prompts. However, current FCO methods struggle with artifacts—unwanted elements in the background that share semantics with the foreground object—due to misalignment between text prompts and visual instances.
To tackle this, the Customized Concept Embedding Diffusion (CCE-Diffusion) framework was introduced, featuring a CCE-Module that customizes concept embeddings to better align with specific visual instances. An Instance-Aware Loss function optimizes this module, while a Semantic-Preserving Prompt Template ensures that the integrity of the original prompt is maintained.
Results from qualitative and quantitative evaluations indicate that CCE-Diffusion significantly reduces artifacts, leading to higher quality outputs. This framework can be integrated as a plug-and-play component with various existing FCO methods, enhancing their performance.