The Long-form Video Question-Answering task requires the comprehension and
analysis of extended video content to respond accurately to questions by
utilizing both temporal and contextual information. In this paper, we present
MM-Screenplayer, an advanced video understanding system with multi-modal
perception capabilities that can convert any video into textual screenplay
representations. Unlike previous storytelling methods, we organize video
content into scenes as the basic unit, rather than just visually continuous
shots. Additionally, we developed a ``Look Back'' strategy to reassess and
validate uncertain information, particularly targeting breakpoint mode.
MM-Screenplayer achieved highest score in the CVPR'2024 LOng-form VidEo
Understanding (LOVEU) Track 1 Challenge, with a global accuracy of 87.5% and a
breakpoint accuracy of 68.8%.

MM-Screenplayer 是一种先进的视频理解系统，具备多模态感知能力，可以将任何视频转换为文本剧本表示形式。与以往的故事叙述方法不同，我们将视频内容以场景为基本单位进行组织，而不仅仅是视觉连续的镜头。此外，我们开发了一种 “回溯” 策略来重新评估和验证不确定信息，特别针对断点模式。MM-Screenplayer 在 CVPR'2024 Long-form Video Understanding（LOVEU）Track 1 挑战中取得了最高分，全球准确率为 87.5％，断点准确率为 68.8％。