HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
Name
1712.09374.pdf
Description
Submitted version
Size
4.41 MB
Format
Adobe PDF
Checksum (MD5)
6442fe0f07866e21d2db3d2c43f0257e
Author(s)
Zhao, Hang
Torralba, Antonio
Torresani, Lorenzo
Yan, Zhicheng
Date Issued
2019
Journal
Proceedings of the IEEE International Conference on Computer Vision
Publisher
IEEE
Citation
Zhao, Hang, Torralba, Antonio, Torresani, Lorenzo and Yan, Zhicheng. 2019. "HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization." Proceedings of the IEEE International Conference on Computer Vision, 2019-October.
Version
Original manuscript
Abstract
© 2019 IEEE. This paper presents a new large-scale dataset for recognition and temporal localization of human actions collected from Web videos. We refer to it as HACS (Human Action Clips and Segments). We leverage consensus and disagreement among visual classifiers to automatically mine candidate short clips from unlabeled videos, which are subsequently validated by human annotators. The resulting dataset is dubbed HACS Clips. Through a separate process we also collect annotations defining action segment boundaries. This resulting dataset is called HACS Segments. Overall, HACS Clips consists of 1.5M annotated clips sampled from 504K untrimmed videos, and HACS Segments contains 139K action segments densely annotated in 50K untrimmed videos spanning 200 action categories. HACS Clips contains more labeled examples than any existing video benchmark. This renders our dataset both a large-scale action recognition benchmark and an excellent source for spatiotemporal feature learning. In our transfer learning experiments on three target datasets, HACS Clips outperforms Kinetics-600, Moments-In-Time and Sports1M as a pretraining source. On HACS Segments, we evaluate state-of-the-art methods of action proposal generation and action localization, and highlight the new challenges posed by our dense temporal annotations.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICCV.2019.00876
