Token Clustering

Token clustering in computer vision aims to improve the efficiency and effectiveness of transformer-based models by grouping semantically similar image regions into clusters, represented by fewer, more informative tokens. Current research focuses on developing dynamic clustering algorithms that adapt to the image content, often integrated within transformer architectures, leading to models like TCFormer and SecViT that achieve improved performance in various tasks including image classification, object detection, and even brain connectome analysis. This approach offers significant advantages by reducing computational costs, enhancing model interpretability, and improving accuracy in challenging scenarios like camouflaged object detection and human-centric visual analysis.