Neural Clustering based Visual Representation Learning

arXiv:2403.17409v1 Announce Type: new
Abstract: We investigate a fundamental aspect of machine vision: the measurement of features, by revisiting clustering, one of the most classic approaches in machine learning and data analysis. Existing visual feature extractors, including ConvNets, ViTs, and MLPs, represent an image as rectangular regions. Though prevalent, such a grid-style paradigm is built upon engineering practice and lacks explicit modeling of data distribution. In this work, we propose feature extraction with clustering (FEC), a conceptually elegant yet surprisingly …

abstract analysis arxiv clustering cs.cv data data analysis feature features grid image machine machine learning machine vision measurement paradigm representation representation learning style type vision visual