  Video question answering (Video QA) presents a powerful testbed for
human-like intelligent behaviors. The task demands new capabilities to
integrate video processing, language understanding, binding abstract linguistic
concepts to concrete visual artifacts, and deliberative reasoning over
spacetime. Neural networks offer a promising approach to reach this potential
through learning from examples rather than handcrafting features and rules.
However, neural networks are predominantly feature-based - they map data to
unstructured vectorial representation and thus can fall into the trap of
exploiting shortcuts through surface statistics instead of true systematic
reasoning seen in symbolic systems. To tackle this issue, we advocate for
object-centric representation as a basis for constructing spatio-temporal
structures from videos, essentially bridging the semantic gap between low-level
pattern recognition and high-level symbolic algebra. To this end, we propose a
new query-guided representation framework to turn a video into an evolving
relational graph of objects, whose features and interactions are dynamically
and conditionally inferred. The object lives are then summarized into resumes,
lending naturally for deliberative relational reasoning that produces an answer
to the query. The framework is evaluated on major Video QA datasets,
demonstrating clear benefits of the object-centric approach to video reasoning.
