Human vision incorporates non-uniform resolution retina, efficient eye
movement strategy, and spiking neural network (SNN) to balance the requirements
in visual field size, visual resolution, energy cost, and inference latency.
These properties have inspired interest in developing human-like computer
vision. However, existing models haven't fully incorporated the three features
of human vision, and their learned eye movement strategies haven't been
compared with human's strategy, making the models' behavior difficult to
interpret. Here, we carry out experiments to examine human visual search
behaviors and establish the first SNN-based visual search model. The model
combines an artificial retina with spiking feature extraction, memory, and
saccade decision modules, and it employs population coding for fast and
efficient saccade decisions. The model can learn either a human-like or a
near-optimal fixation strategy, outperform humans in search speed and accuracy,
and achieve high energy efficiency through short saccade decision latency and
sparse activation. It also suggests that the human search strategy is
suboptimal in terms of search speed. Our work connects modeling of vision in
neuroscience and machine learning and sheds light on developing more
energy-efficient computer vision algorithms.

通过实验证明人类视觉搜索行为并建立了第一个基于 SNN 的视觉搜索模型，该模型结合了人工视网膜结构与尖峰特征提取、记忆和注视决策模块，通过群体编码实现快速高效的注视决策，能够学习类似人类或接近最佳的凝视策略，在搜索速度和准确性方面优于人类，并通过短注视决策延迟和稀疏激活实现高能效性。这一工作将神经科学和机器学习中的视觉建模联系起来，并为开发更高能效的计算机视觉算法提供了启示。