Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
Mingfei Han Linjie Yang Xiaojun Chang Lina Yao Heng Wang
A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark \dataset with detailed shot-level captions, comprehensive video summaries and question-answering pairs. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations. We design several distinct tasks including single-shot video captioning, multi-shot video summarization, and multi-shot video question answering. Preliminary experiments show some challenges to generate a long and comprehensive video summary for multi-shot videos. Nevertheless, the generated imperfect summaries can already achieve competitive performance on existing video understanding tasks such as video question-answering, promoting an under-explored setting of video understanding with detailed summaries.
vision language model, video question answering, video captioning, multi-shot videos
Shot2Story presents a large-scale dataset with 43,000 multi-shot videos and 188,000 manually annotated shots, offering detailed visual/audio captions, summaries, and QA pairs to advance multi-shot video understanding.