Pixel Embeddings

Pixel embeddings represent individual image pixels as vectors in a high-dimensional space, aiming to capture their semantic and contextual information for various computer vision tasks. Current research focuses on improving the quality and robustness of these embeddings, often employing techniques like contrastive learning, random walks on pixel manifolds, and clustering to address challenges such as background noise and the preservation of spatial relationships. These advancements are driving progress in diverse applications, including anomaly detection in driving scenes, few-shot segmentation, and improved image generation and semantic understanding.