Neuro-Symbolic AI in Computer Vision: Toward More Interpretable, Efficient, Generalized, and Logical Visual Understanding Systems

Computer vision has evolved dramatically from traditional handcrafted image processing methods to advanced deep learning models. However, despite achieving notable results, these purely statistical methods often suffer from limitations in interpretability, data efficiency, generalization, and reasoning capabilities. Neuro-Symbolic (NeSy) AI has emerged as a promising paradigm that integrates the powerful pattern recognition of neural networks with the structured, logical reasoning of symbolic systems. This paper provides a comprehensive introduction to NeSy applications in computer vision, covering tasks such as image classification, object detection, scene understanding, and action recognition. We explore key NeSy frameworks, including Logic Tensor Networks (LTNs), highlighting their ability to improve interpretability, robustness, and reasoning. Finally, we discuss the challenges and future directions this promising hybrid approach poses toward explainable and trustworthy computer vision solutions.