TIG: A Multitask Temporal Interval Guided Framework for Key Frame Detection
Author:
Affiliation:
1. School of Electronic Science and Engineering, Nanjing University
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/9/E107.D_2024EDP7031/_pdf
Reference48 articles.
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2. [2] G. Yasmin, S. Chowdhury, J. Nayak, P. Das, and A.K. Das, “Key moment extraction for designing an agglomerative clustering algorithm-based video summarization framework,” Neural Computing and Applications, vol.35, no.7, pp.4881-4902, 2023. 10.1007/s00521-021-06132-1
3. [3] T. Bhattacharjee, S. Saha, A. Konar, and A.K. Nagar, “Static video summarization using artificial bee colony optimization,” 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp.777-784, 2018. 10.1109/ssci.2018.8628784
4. [4] Z. Shou, J. Pan, J. Chan, K. Miyazawa, H. Mansour, A. Vetro, X. Giro-i-Nieto, and S.-F. Chang, “Online detection of action start in untrimmed, streaming videos,” Proc. European Conference on Computer Vision (ECCV), vol.11207, pp.534-551, Sept. 2018. 10.1007/978-3-030-01219-9_33
5. [5] M. Gao, M. Xu, L. Davis, R. Socher, and C. Xiong, “Startnet: Online detection of action start in untrimmed videos,” 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp.5541-5550, 2019. 10.1109/iccv.2019.00564
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