Human Vision-Like Robust Object Recognition

Peng Kang, Hao Hu, Srutarshi Banerjee, Henry Chopp, Aggelos K. Katsaggelos, Oliver Cossairt

Abstract: Previous research always solely utilizes Artificial Neural Networks (ANNs) or Spiking Neural Networks (SNNs) for object recognition. However, evidence in neuroscience suggests that the visual processing in human vision is performed hierarchically in the combination of analog and digital processing. To construct a more human vision-like object recognition system, we propose a general hierarchical ANN-SNN model. We evaluate our model and its variants on two popular datasets to show its effectiveness, robustness, efficiency, and generality. Extensive experiments clearly demonstrate the superiority of our proposed models for robust object recognition.