Diffusion Feature

Diffusion features, extracted from pretrained diffusion models, are emerging as powerful representations for various computer vision tasks. Current research focuses on leveraging these features for improved controllability in image and video editing, zero-shot semantic segmentation, and robust object detection, often employing techniques like frequency band substitution, feature correspondence analysis, and diffusion model fine-tuning. This work demonstrates the potential of diffusion features to enhance the performance and generalizability of existing methods across diverse applications, particularly in scenarios with limited labeled data or unseen objects.