Towards Temporal Event Detection: A Dataset and Benchmarks
Zhenguo Yang, Haizhong Zhu, Zhiwei Guo, Han X. Lin, Zehang Lin, Qing Li, Wenyin Liu · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
In this paper, we present a new dataset with the target of advancing temporal real-world event detection from video-sharing social media. The collected temporal event dataset (TED) has certain characteristics. 1) Certainty of event labels that can be recognized as real-world happening. 2) Wide range of event categories covering public security, natural disasters, elections, sports and entertainment events, etc. 3) High overlap among event topics ensuring the difficulties in distinguishing the labels. 4) Multiple data modalities involving textual, acoustic, and visual information, etc. More specifically, two scenarios are defined based on the close or open domains, i.e., temporal event detection without/with new events, denoted as TED-W and TED-N, respectively. For comparisons, a few benchmarks are investigated on the two scenarios with the dataset.