arxiv preprint – MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding

In this episode, we discuss MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding by Hongjie Zhang, Yi Liu, Lu Dong, Yifei Huang, Zhen-Hua Ling, Yali Wang, Limin Wang, Yu Qiao. The newly introduced dataset MoVQA aims to enhance the evaluation of AI systems’ understanding of long-form video content, such as movies, addressing the limitations of previous datasets that did not fully capture the complexity and lengthy nature of such content. It challenges AI models with a more realistic range of temporal lengths and multimodal questions to mimic human-level comprehension from a moviegoer’s perspective. Initial experiments with MoVQA show that current methods struggle as video and clue lengths increase, indicating substantial room for improvement in long-form video understanding AI research.