  Video Question Answering (VideoQA) has emerged as a vital tool to evaluate
agents' ability to understand human daily behaviors. Despite the recent success
of large vision language models in many multi-modal tasks, complex situation
reasoning over videos involving multiple human-object interaction events still
remains challenging. In contrast, humans can easily tackle it by using a series
of episode memories as anchors to quickly locate question-related key moments
for reasoning. To mimic this effective reasoning strategy, we propose the
Glance-Focus model. One simple way is to apply an action detection model to
predict a set of actions as key memories. However, these actions within a
closed set vocabulary are hard to generalize to various video domains. Instead
of that, we train an Encoder-Decoder to generate a set of dynamic event
memories at the glancing stage. Apart from using supervised bipartite matching
to obtain the event memories, we further design an unsupervised memory
generation method to get rid of dependence on event annotations. Next, at the
focusing stage, these event memories act as a bridge to establish the
correlation between the questions with high-level event concepts and low-level
lengthy video content. Given the question, the model first focuses on the
generated key event memory, then focuses on the most relevant moment for
reasoning through our designed multi-level cross-attention mechanism. We
conduct extensive experiments on four Multi-Event VideoQA benchmarks including
STAR, EgoTaskQA, AGQA, and NExT-QA. Our proposed model achieves
state-of-the-art results, surpassing current large models in various
challenging reasoning tasks. The code and models are available at
https://github.com/ByZ0e/Glance-Focus.
