Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Diffusion models excel at image generation but struggle with semantic details and in-context learning.
• High computational costs and slow inference limit real-time applications of current diffusion models.
• Existing methods lack robust contextual understanding and adaptability for multi-task scenarios.

Purpose of the Study:

• To introduce Underlying Semantic Diffusion (US-Diffusion), an enhanced diffusion model.
• To improve semantic learning, computational efficiency, and in-context learning capabilities.
• To address limitations in contextual understanding and image generation quality.

Main Methods:

• Separate & Gather Adapter (SGA) decouples input conditions for multi-task learning and generalization.
• Feedback-Aided Learning (FAL) framework uses feedback signals for semantic detail capture and adaptation.
• Efficient Sampling Strategy (ESS) optimizes training and inference speed.

Main Results:

• US-Diffusion achieves superior performance on Map2Image and Image2Map tasks, outperforming state-of-the-art methods.
• Demonstrates significant reductions in FID (7.47) and RMSE (0.026) with 9.45x faster inference.
• Exhibits enhanced training efficiency, robust in-context learning, and adaptability to new datasets and tasks.

Conclusions:

• US-Diffusion offers a powerful solution for controllable image generation and dense prediction.
• The model shows significant improvements in efficiency, semantic understanding, and generalization.
• US-Diffusion represents a robust and adaptable advancement in diffusion model capabilities for diverse visual domains.