Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator NetworksShow others and affiliations
2024 (English)In: 2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2024Conference paper, Published paper (Refereed)
Abstract [en]
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 [1]. The integration of sparse coding with the resonator network increases the capacity of distributed representations and reduces collisions in the combinatorial search space during factorization. We find that for this problem the resonator network is capable of fast and accurate vector factorization, and we develop a confidence-based metric that assists in tracking the convergence of the resonator network.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2024.
Keywords [en]
Convolution; Factorization; Network coding; Vectors; Combinatorial search; Computing-in-superposition; Feature representation; Hyperdimensional computing; Resonator network; Sparse coding; Vector factorizations; Vector symbolic architecture; Visual scene; Visual scene understanding; Resonators
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ri:diva-74865DOI: 10.1109/NICE61972.2024.10549719Scopus ID: 2-s2.0-85196224848ISBN: 9798350390582 (electronic)OAI: oai:DiVA.org:ri-74865DiVA, id: diva2:1890699
Conference
2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024. La Jolla, USA. 23 April 2024 through 26 April 2024
Note
The work of CJK was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate (NDSEG) Fellowship Program. The work of SM was carried out as part of the ARPE program of ENS Paris-Saclay. The work of DK and BAO was supported in part by Intel's THWAI program. The work of CJK and BAO was supported by the Center for the Co-Design of Cognitive Systems (CoCoSys), one of seven centers in JUMP 2.0, a Semiconductor Research Corporation (SRC) program sponsored by DARPA. DK has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 839179.
2024-08-202024-08-202024-08-20Bibliographically approved