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.

为了解决当前长篇视频理解数据集的局限性，研究引入了 MoVQA 数据集和基准评估，以评估多模态系统对多级时间长度的多样化认知能力，同时设计了从观影者角度出发的多模态问答来评估模型在感知和认知多个方面的能力。经过分析，各种方法在处理视频和线索长度不断增加时性能显著下降，基准方法有所改进，但在挑战性的 MoVQA 数据集上仍有充分的提升空间。预计 MoVQA 提供新的视角，并激励针对长篇视频理解研究的启发性工作。