基于主动有效编码的中心凹视觉和周边视觉的学习分层融合
Learning Hierarchical Integration of Foveal and Peripheral Vision for Vergence Control by Active Efficient Coding
摘要：
主动高效编码（AEC）框架粗略地解释了视觉处理和眼球运动的联合发展，例如，双眼视差选择性神经元和融合性聚散的出现，使左右眼图像对齐的分离性眼球运动。会聚可以由中央凹和周边的信息驱动，它们发挥互补作用。高分辨率的中央凹可以驱动精确的短距离运动。较低分辨率的外围支持较粗的长距离移动。中央凹和外围也可能包含冲突的信息，例如由于不同深度处的对象。虽然过去的AEC模型确实整合了外周和中央凹信息，但它们没有明确考虑这些特征。我们在这里提出了一个两级分层的方法，这样做。底层从中央凹和周边区域生成不同的聚散动作。顶层选择一个。我们证明了层次化的方法在现实环境中比以前的方法表现得更好，表现出更好的对齐和更少的振荡。
英文摘要：
The active efficient coding (AEC) framework parsimoniously explains the joint development of visual processing and eye movements, e.g., the emergence of binocular disparity selective neurons and fusional vergence, the disjunctive eye movements that align left and right eye images. Vergence can be driven by information in both the fovea and periphery, which play complementary roles. The high resolution fovea can drive precise short range movements. The lower resolution periphery supports coarser long range movements. The fovea and periphery may also contain conflicting information, e.g. due to objects at different depths. While past AEC models did integrate peripheral and foveal information, they did not explicitly take into account these characteristics. We propose here a two-level hierarchical approach that does. The bottom level generates different vergence actions from foveal and peripheral regions. The top level selects one. We demonstrate that the hierarchical approach performs better than prior approaches in realistic environments, exhibiting better alignment and less oscillation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
