RESTHT: relation-enhanced spatial–temporal hierarchical transformer for video captioning
Author:
Funder
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03350-1.pdf
Reference46 articles.
1. Venugopalan, S., Rohrbach, M., Donahue, J., et al.: Sequence to sequence-video to text[C]. In: Proceedings of the IEEE international conference on computer vision. 2015: 4534–4542. https://doi.org/10.1109/iccv.2015.515
2. Pan, P., Xu, Z., Yang, Y., et al.: Hierarchical recurrent neural encoder for video representation with application to captioning[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1029–1038. https://doi.org/10.1109/cvpr.2016.117
3. Peng, Y., Wang, C., Pei, Y., et al.: Video captioning with global and local text attention[J]. Vis. Comput. 38(12), 4267–4278 (2022). https://doi.org/10.1007/s00371-021-02294-0
4. Hu, Y., Chen, Z., Zha, Z. J., et al.: Hierarchical global-local temporal modeling for video captioning[C]. In: Proceedings of the 27th ACM International Conference on Multimedia. 2019: 774–783. https://doi.org/10.1145/3343031.3351072
5. Yan, C., Tu, Y., Wang, X., et al.: STAT: Spatial-temporal attention mechanism for video captioning[J]. IEEE Trans. Multim. 22(1), 229–241 (2019). https://doi.org/10.1109/tmm.2019.2924576
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