The lecture emphasises application areas where the processing and explainable semantic interpretation of (potentially large
volumes of) dynamic visuospatial imagery is central, e.g., for commonsense scene understanding; visual cognition for cognitive robotics / HRI,
autonomous driving; narrative interpretation from the viewpoints of visuoauditory perception & digital media design, semantic interpretation
of multimodal human-behavioural data.
The lecture will highlight Deep (Visuospatial) Semantics, denoting the existence of systematically formalised declarative AI methods –e.g., per-
taining to reasoning about space and motion– supporting semantic (visual) question-answering, relational learning, non-monotonic (visuospa-
tial) abduction, and simulation of embodied interaction. The lecture demonstrates the integration of methods from knowledge representation
and computer vision with a focus on (combining) reasoning & learning about space, action, motion, and (multimodal) interaction. This is pre-
sented in the backdrop of areas as diverse as autonomous driving, cognitive robotics, eye-tracking driven visual perception research (e.g., for
visual art, architecture design, cognitive media studies), and psychology & behavioural research domains where data-centred analytical methods
are gaining momentum. The lecture covers both applications and basic methods concerned with topics such as: explainable visual perception,
semantic video understanding, language generation from video, declarative spatial reasoning, and computational models of narrative. The lec-
ture will position an emerging line of research that brings together a novel & unique combination of research methodologies, academics, and
communities encompassing AI, ML, Vision, Cognitive Linguistics, Psychology, Visual Perception, and Spatial Cognition and Computation.