Deep Semantics for Explainable Visuospatial Intelligence: Perspectives on Integrating Commonsense Spatial Abstractions and Low-Level Neural Features

High-level semantic interpretation of dynamic visual imagery calls for general and systematic methods integrating techniques in knowledge representation and computer vision. Towards this, we position deep semantics, denoting the existence of declarative models such as those pertaining space and motion, and corresponding formalisation and methods supporting domain-independent explainability capabilities such as semantic question-answering, relational and relationally-driven visuospatial learning, and non-monotonic visuospatial abduction. Rooted in recent work, we summarise and report the status quo on deep visuospatial semantics, and our approach to neurosymbolic integration and explainable visuo-spatial computing in that context, with developed methods and tools in diverse settings such as behavioural research in psychology, art and social sciences, and autonomous driving.

Authors

• Mehul Bhatt
• Jakob Suchan
• Srikrishna Varadarajan

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