对象识别中的上下文反:生物启发的计算模型和人类行为研究
Elahe Soltandoost1, Karim Rajaei2, Reza Ebrahimpour3
1University of Padova, Department of Information Engineering, Via Gradenigo 6/b, Padova, 35131, Veneto, Italy; Shahid Rajaei University, Faculty of Engineering, Lavizan, Shahid Babaei Highway, Tehran, 16788-15811, Iran.
Vision research
|August 31, 2025
概括
一个新的基于上下文的计算模型 (CBM) 提高了对象识别与场景上下文. 虽然有效,但人类的视觉感知更多地依赖于内在的对象处理,尤其是遮蔽.
科学领域:
- 计算神经科学
- 计算机视觉
- 认知心理学
背景情况:
- 场景背景显著影响视觉感知和物体识别,特别是在具有挑战性的条件下.
- 建议使用自上而下的机制来调节对象选择性大脑区域的场景信息,但尚未完全理解.
研究的目的:
- 引入一个基于生物背景的计算模型 (CBM) 用于对象识别.
- 研究明确反机制的作用,将场景背景整合到对象识别中.
- 将CBM性能与标准的前模型和人类感知进行比较.
主要方法:
- 开发了一个基于上下文的计算模型 (CBM),有两个路径:对象特征的Object_CNN和场景上下文的Place_CNN.
- 对象识别任务的CBM与AlexNet (标准的前模型) 进行比较.
- 进行行为实验以比较人类对象识别与CBM和AlexNet的性能.
主要成果:
- 在对象识别方面,CBM显著超过了AlexNet,证明了环境反对降低输入的好处.
- 人类参与者从一致的背景中获得了适度的益处,特别是高水平的遮.
- 即使没有上下文,人类的识别仍然很强大,这表明全球形状处理和局部反复的过程具有主导作用.
结论:
- 语境反可以提高对象识别的计算模型性能.
- 人类的视觉感知采用不同的策略,主要依赖于内在的对象处理和局部的反复机制.
- 未来的模型应该将情境敏感的反与本地反复的过程相结合,以更好地模仿人类的视觉性.
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