# Zero-Shot Long-Form Video Understanding through Screenplay

arXiv: https://arxiv.org/abs/2406.17309 · HTML: https://mlsanity.com/paper/2406.17309/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 11 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [MovieChat: From Dense Token to Sparse Memory for Long Video Understanding](https://mlsanity.com/paper/2307.16449/) (2023)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [A Simple LLM Framework for Long-Range Video Question-Answering](https://mlsanity.com/paper/2312.17235/) (2023)
- [MM-VID: Advancing Video Understanding with GPT-4V(ision)](https://mlsanity.com/paper/2310.19773/) (2023)
- [Exploring Diverse In-Context Configurations for Image Captioning](https://mlsanity.com/paper/2305.14800/) (2023)

## Built on by (most cited first, 6 of 6)

- [VideoNSA: Native Sparse Attention Scales Video Understanding](https://mlsanity.com/paper/2510.02295/) (2025)
- [AdaRD-key: Adaptive Relevance-Diversity Keyframe Sampling for Long-form Video understanding](https://mlsanity.com/paper/2510.02778/) (2025)
- [VEU-Bench: Towards Comprehensive Understanding of Video Editing](https://mlsanity.com/paper/2504.17828/) (2025)
- [Parameter-free Video Segmentation for Vision and Language Understanding](https://mlsanity.com/paper/2503.01201/) (2025)
- [Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation](https://mlsanity.com/paper/2505.06594/) (2025)
- [ViRC: Enhancing Visual Interleaved Mathematical CoT with Reason Chunking](https://mlsanity.com/paper/2512.14654/) (2025)

## Abstract (arXiv)

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%.