The field of video question answering is moving towards more nuanced and human-like understanding, with a focus on implicit reasoning and context-based inference. Current systems are being challenged to move beyond surface-level visual cues and instead integrate information across time and context to construct coherent narratives. This shift is driven by the need for more robust and interpretable models that can capture the complexities of real-world scenarios. Notable papers in this area include:

• ImplicitQA, which introduces a novel benchmark for implicit reasoning in video question answering, and
• DIVE, which presents an iterative reasoning approach that achieves highly accurate and contextually appropriate answers to complex queries.
• Box-QAymo, which proposes a hierarchical evaluation protocol for spatial and temporal reasoning over user-specified objects in autonomous driving scenarios.