Dual-Pathway Neural Networks: Harnessing Scene and Object Pathways for Enhanced Visual Understanding

Standard artificial neural networks (ANNs) often struggle with generalization due to their reliance on surface-level cues, which can lead to suboptimal performance. Drawing inspiration from the distinct processing pathways for scenes and objects in the human brain, we explore the interactions between scene and object and introduce a dual-modality architecture aimed at emulating this cognitive processing mechanism within ANNs. Our approach features separate encodings for scene and object modalities, which are fused to facilitate enhanced visual understanding. By optimizing object recognition and scene reconstruction objectives, our architecture efficiently encodes scene and object information crucial for holistic representation learning. Empirical validation demonstrates significant improvements in generalization, lifelong learning, and adversarial robustness compared to conventional architectures. These findings underscore the potential of integrating biological insights into AI systems to bridge the gap between artificial and biological intelligence.