M. Otani, Y. Nakashima, E. Rahtu and J. Heikkilä, "Rethinking the Evaluation of Video Summaries," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 7588-7596. doi: 10.1109/CVPR.2019.00778
Rethinking the evaluation of video summaries
|Author:||Otani, Mayu1; Nakashima, Yuta2; Rahtu, Esa3;|
4University of Oulu
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003238864
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-03-23
Video summarization is a technique to create a short skim of the original video while preserving the main stories/content. There exists a substantial interest in automatizing this process due to the rapid growth of the available material. The recent progress has been facilitated by public benchmark datasets, which enable easy and fair comparison of methods. Currently the established evaluation protocol is to compare the generated summary with respect to a set of reference summaries provided by the dataset. In this paper, we will provide in-depth assessment of this pipeline using two popular benchmark datasets. Surprisingly, we observe that randomly generated summaries achieve comparable or better performance to the state-of-the-art. In some cases, the random summaries outperform even the human generated summaries in leave-one-out experiments. Moreover, it turns out that the video segmentation, which is often considered as a fixed pre-processing method, has the most significant impact on the performance measure. Based on our observations, we propose alternative approaches for assessing the importance scores as well as an intuitive visualization of correlation between the estimated scoring and human annotations.
|Pages:||7588 - 7596|
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 15-20 June 2019, Long Beach, USA
IEEE/CVF Conference on Computer Vision and Pattern Recognition
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work was partly supported by JSPS KAKENHI Grant Nos. 16K16086 and 18H03264.
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