通过基于人工智能的图形-地面隔离表示来接近人类视觉感知
Chakkai Yip1, Ezekiel Moroze1, Shigeaki Nishina2
1Computational Neuroscience and Vision Laboratory, Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States.
Frontiers in psychology
|March 16, 2026
概括
卷积神经网络 (CNN) 可以从视觉上下文推断图形-地面感知的边界所有权 (BOS). 几何配置,而不仅仅是边缘,对于这个过程至关重要,即使视觉输入受损.
科学领域:
- 计算神经科学是一种计算神经科学.
- 计算机视觉 计算机视觉 计算机视觉
- 视觉感知 视觉感知 视觉感知
背景情况:
- 图形-背景感知对于对象识别至关重要.
- 边境所有权分配 (BOS) 的计算基础仍然不清楚.
- 了解BOS有助于开发人工视觉系统.
研究的目的:
- 调查卷积神经网络 (CNN) 架构如何推断边界所有权 (BOS).
- 确定情境信息在BOS推断中在降解条件下的作用.
- 探索与BOS相关的CNN中视觉特征的层次处理.
主要方法:
- 训练了多个CNN架构的重叠/阻塞刺激.
- 在系统降解的轮上测试了CNN的性能.
- 分析了网络表示,以了解特征层次结构.
主要成果:
- CNNs从feedforward计算中推断出BOS,即使具有退化的轮.
- 性能强烈依赖于类似交叉点的配置,突出地形背景.
- 精度是线性缩放的,来自碎片边界的上下文信息.
- 观察到从局部边缘到BOS特定特征的层次进展.
结论:
- 层次化的前期加工可以解释BOS推断的某些方面.
- 对BOS而言,几何上下文比孤立的边缘更有影响力.
- 额外的机制,如反可能是必要的强大的图形-地面分离.
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