Toward an Over-parameterized Direct-Fit Model of Visual Perception

• URL: http://arxiv.org/abs/2210.03850v2
• Date: Tue, 11 Oct 2022 15:37:43 GMT
• Title: Toward an Over-parameterized Direct-Fit Model of Visual Perception
• Authors: Xin Li
• Abstract summary: In this paper, we highlight the difference in parallel and sequential binding mechanisms between simple and complex cells. A new proposal for abstracting them into space partitioning and composition is developed. We show how it leads to a dynamic programming (DP)-like approximate nearest-neighbor search based on $ell_infty$-optimization.
• Score: 5.4823225815317125
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this paper, we revisit the problem of computational modeling of simple and complex cells for an over-parameterized and direct-fit model of visual perception. Unlike conventional wisdom, we highlight the difference in parallel and sequential binding mechanisms between simple and complex cells. A new proposal for abstracting them into space partitioning and composition is developed as the foundation of our new hierarchical construction. Our construction can be interpreted as a product topology-based generalization of the existing k-d tree, making it suitable for brute-force direct-fit in a high-dimensional space. The constructed model has been applied to several classical experiments in neuroscience and psychology. We provide an anti-sparse coding interpretation of the constructed vision model and show how it leads to a dynamic programming (DP)-like approximate nearest-neighbor search based on $\ell_{\infty}$-optimization. We also briefly discuss two possible implementations based on asymmetrical (decoder matters more) auto-encoder and spiking neural networks (SNN), respectively.

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