Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks

arXiv:2404.19126v1 Announce Type: new
Abstract: We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequently factorized to parse scene content. The sparse feature representation is learned from image statistics via convolutional sparse coding, while scene parsing is performed by a resonator network. The integration of sparse coding with the resonator network increases the capacity of distributed representations and reduces collisions in the combinatorial …

abstract analysis arxiv coding convolutional cs.cv cs.ne encoding factorization feature image networks recognition representation statistics type vector via visual