Zero-Shot Long-Form Video Understanding through Screenplay
원문 초록
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%.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
video-SALMONN 2: Captioning-Enhanced Audio-Visual Large Language Models
EgoVLM: Policy Optimization for Egocentric Video Understanding
VUDG: A Dataset for Video Understanding Domain Generalization
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.