  This paper strives to solve complex video question answering (VideoQA) which
features long video containing multiple objects and events at different time.
To tackle the challenge, we highlight the importance of identifying
question-critical temporal moments and spatial objects from the vast amount of
video content. Towards this, we propose a Spatio-Temporal Rationalization
(STR), a differentiable selection module that adaptively collects
question-critical moments and objects using cross-modal interaction. The
discovered video moments and objects are then served as grounded rationales to
support answer reasoning. Based on STR, we further propose TranSTR, a
Transformer-style neural network architecture that takes STR as the core and
additionally underscores a novel answer interaction mechanism to coordinate STR
for answer decoding. Experiments on four datasets show that TranSTR achieves
new state-of-the-art (SoTA). Especially, on NExT-QA and Causal-VidQA which
feature complex VideoQA, it significantly surpasses the previous SoTA by 5.8\%
and 6.8\%, respectively. We then conduct extensive studies to verify the
importance of STR as well as the proposed answer interaction mechanism. With
the success of TranSTR and our comprehensive analysis, we hope this work can
spark more future efforts in complex VideoQA. Code will be released at
https://github.com/yl3800/TranSTR.
