Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
Mingfei Han, Linjie Yang, Xiaojun Chang, Lina Yao, Heng Wang
Abstract
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.
BibTeX
@inproceedings{
han2025shotstory,
title={Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos},
author={Mingfei Han and Linjie Yang and Xiaojun Chang and Lina Yao and Heng Wang},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=FZv3kPHTtB}
}