MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie
  Understanding
Hongjie Zhang Yi Liu Lu Dong Yifei Huang Zhen-Hua Ling Yali Wang Limin Wang Yu Qiao
While several long-form VideoQA datasets have been introduced, the length of
both videos used to curate questions and sub-clips of clues leveraged to answer
those questions have not yet reached the criteria for genuine long-form video
understanding. Moreover, their QAs are unduly narrow and modality-biased,
lacking a wider view of understanding long-term video content with rich
dynamics and complex narratives. To remedy this, we introduce MoVQA, a
long-form movie question-answering dataset, and benchmark to assess the diverse
cognitive capabilities of multimodal systems rely on multi-level temporal
lengths, with considering both video length and clue length. Additionally, to
take a step towards human-level understanding in long-form video, versatile and
multimodal question-answering is designed from the moviegoer-perspective to
assess the model capabilities on various perceptual and cognitive axes.Through
analysis involving various baselines reveals a consistent trend: the
performance of all methods significantly deteriorate with increasing video and
clue length. Meanwhile, our established baseline method has shown some
improvements, but there is still ample scope for enhancement on our challenging
MoVQA dataset. We expect our MoVQA to provide a new perspective and encourage
inspiring works on long-form video understanding research.