Perceptual Adjustment Query

Perceptual adjustment queries (PAQs) represent a novel approach to gathering human feedback for machine learning models, focusing on how humans perceive and adjust to changes in sensory input like color, sound, or visual features. Current research explores PAQs within various model architectures, including latent diffusion models for voice modification and equivariant neural networks for handling color variations, aiming to improve model robustness and alignment with human perception. This work is significant because it addresses the limitations of traditional methods by directly incorporating human perceptual judgments, leading to more accurate and user-friendly systems in applications ranging from image and video processing to robotics and voice synthesis.