Fetching the paper…
Reading the bibliography…
Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie.
P. Papalampidi, F. Keller, L. Frermann, and M. Lapata, “Screenplay summarization using latent narrative structure,” in Proceedings of the 58th Annual Meeting of the ACL , 2020, pp. 1920–1933
1933
Earlier work this paper cites.
E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik , vol. 1, no. 1, pp. 269–271, 1959
1959
Earlier work this paper cites.
C. S. Myers and L. R. Rabiner, “A comparative study of several dynamic time-warping algorithms for connected-word recognition,” Bell System Technical Journal , vol. 60, no. 7, pp. 1389–1409, 1981
1981
Earlier work this paper cites.
K. Thompson, Storytelling in the new Hollywood: Understanding classical narrative technique . Harvard University Press, 1999
1999
Earlier work this paper cites.
R. Mihalcea and P. Tarau, “Textrank: Bringing order into text,” in Proceedings of the 2004 conference on empirical methods in natural language processing , 2004, pp. 404–411
2004
Earlier work this paper cites.
A. F. Smeaton, B. Lehane, N. E. O’Connor, C. Brady, and G. Craig, “Automatically selecting shots for action movie trailers,” in Proceedings of the 8th ACM international workshop on Multimedia information retrieval , 2006, pp. 231–238
2006
Earlier work this paper cites.
C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,” Language resources and evaluation , vol. 42, no. 4, p. 335, 2008
2008
Earlier work this paper cites.
M. Grimm, K. Kroschel, and S. Narayanan, “The Vera am Mittag German audio-visual emotional speech database,” in ICME . IEEE, 2008, pp. 865–868
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
G. Irie, T. Satou, A. Kojima, T. Yamasaki, and K. Aizawa, “Automatic trailer generation,” in Proceedings of the 18th ACM international conference on Multimedia , 2010, pp. 839–842
2010
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , 2010, pp. 297–304
2010
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Ba and R. Caruana, “Do deep nets really need to be deep?” in Proceedings of the Advances in Neural Information Processing Systems , Montreal, Quebec, Canada, 2014, pp. 2654–2662
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Tapaswi, M. Bauml, and R. Stiefelhagen, “Book2movie: Aligning video scenes with book chapters,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 1827–1835
2015
Earlier work this paper cites.
A. Rohrbach, M. Rohrbach, N. Tandon, and B. Schiele, “A dataset for movie description,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3202–3212
2015
Earlier work this paper cites.
H. Xu, Y. Zhen, and H. Zha, “Trailer generation via a point process-based visual attractiveness model,” in Proceedings of the 24th International Conference on Artificial Intelligence , 2015, pp. 2198–2204
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” Advances in Neural Information Processing Systems , vol. 28, pp. 2224–2232, 2015
2015
Earlier work this paper cites.
P. J. Gorinski and M. Lapata, “Movie script summarization as graph-based scene extraction,” in Proceedings of the 2015 Conference of the NAACL: Human Language Technologies . Denver, Colorado: ACL, May–Jun. 2015, pp. 1066–1076
2015
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
Earlier work this paper cites.
J. Louviere, T. Flynn, and A. A. J. Marley, Best-worst scaling: Theory, methods and applications , 01 2015
2015
Earlier work this paper cites.
S. Bilakhia, S. Petridis, A. Nijholt, and M. Pantic, “The MAHNOB mimicry database: A database of naturalistic human interactions,” Pattern Recognition Letters , vol. 66, pp. 52–61, 2015, pattern Recognition in Human Computer Interaction
2015
Earlier work this paper cites.
J. E. Cutting, “Narrative theory and the dynamics of popular movies,” Psychonomic Bulletin and review , vol. 23, no. 6, pp. 1713–1743, 2016
2016
Earlier work this paper cites.
M. Tapaswi, Y. Zhu, R. Stiefelhagen, A. Torralba, R. Urtasun, and S. Fidler, “Movieqa: Understanding stories in movies through question-answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4631–4640
2016
Earlier work this paper cites.
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley, “Molecular graph convolutions: moving beyond fingerprints,” Journal of computer-aided molecular design , vol. 30, no. 8, pp. 595–608, 2016
2016
Earlier work this paper cites.
M. Hauge, Storytelling Made Easy: Persuade and Transform Your Audiences, Buyers, and Clients – Simply, Quickly, and Profitably . Indie Books International, 2017
2017
Cited alongside, same era.
J. R. Smith, D. Joshi, B. Huet, W. Hsu, and J. Cota, “Harnessing ai for augmenting creativity: Application to movie trailer creation,” in Proceedings of the 25th ACM international conference on Multimedia , 2017, pp. 1799–1808
2017
Cited alongside, same era.
M. Seo, A. Kembhavi, A. Farhadi, and H. Hajishirzi, “Bidirectional attention flow for machine comprehension,” in ICLR , 2017
2017
Cited alongside, same era.
C. J. Maddison, A. Mnih, and Y. W. Teh, “The concrete distribution: A continuous relaxation of discrete random variables,” in 5th ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings , 2017
2017
Cited alongside, same era.
L. Wang, D. Liu, R. Puri, and D. N. Metaxas, “Learning trailer moments in full-length movies with co-contrastive attention,” in European Conference on Computer Vision . Springer, 2020, pp. 300–316
2020
Later among the works it cites.
J. Lei, L. Yu, T. L. Berg, and M. Bansal, “Tvr: A large-scale dataset for video-subtitle moment retrieval,” in European Conference on Computer Vision . Springer, 2020, pp. 447–463
2020
Later among the works it cites.
M. Bain, A. Nagrani, A. Brown, and A. Zisserman, “Condensed movies: Story based retrieval with contextual embeddings,” in Proceedings of the Asian Conference on Computer Vision , 2020
2020
Later among the works it cites.
J. Liu, W. Chen, Y. Cheng, Z. Gan, L. Yu, Y. Yang, and J. Liu, “Violin: A large-scale dataset for video-and-language inference,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 900–10 910
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Cited alongside, same era.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1492–1500
2017
Cited alongside, same era.
J. Carreira and A. Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, 2017, pp. 4724–4733
2017
Cited alongside, same era.
J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio set: An ontology and human-labeled dataset for audio events,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2017, pp. 776–780
2017
Cited alongside, same era.
Y. Li, H. Su, X. Shen, W. Li, Z. Cao, and S. Niu, “Dailydialog: A manually labelled multi-turn dialogue dataset,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2017, pp. 986–995
2017
Cited alongside, same era.
M. Abdul-Mageed and L. Ungar, “EmoNet: Fine-grained emotion detection with gated recurrent neural networks,” in Proceedings of the 55th Annual Meeting of the ACL (Volume 1: Long Papers) . Vancouver, Canada: ACL, Jul. 2017, pp. 718–728. [Online]. Available: https://aclanthology.org/P17-1067
2017
Cited alongside, same era.
Q. Huang, Y. Xiong, A. Rao, J. Wang, and D. Lin, “Movienet: A holistic dataset for movie understanding,” in European Conference on Computer Vision . Springer, 2020, pp. 709–727
2020
Later among the works it cites.
H. Gu, S. Petrangeli, and V. Swaminathan, “Sumbot: Summarize videos like a human,” in 2020 IEEE International Symposium on Multimedia (ISM) . IEEE, 2020, pp. 210–217
2020
Later among the works it cites.
A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic, and A. Zisserman, “End-to-end learning of visual representations from uncurated instructional videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9879–9889
2020
Later among the works it cites.
H. Kim, Z. Tang, and M. Bansal, “Dense-caption matching and frame-selection gating for temporal localization in videoqa,” in Proceedings of the 58th Annual Meeting of the ACL , 2020, pp. 4812–4822
2020
Later among the works it cites.
S. Sun, Z. Gan, Y. Fang, Y. Cheng, S. Wang, and J. Liu, “Contrastive distillation on intermediate representations for language model compression,” in EMNLP , 2020, pp. 498–508
2020
Later among the works it cites.
B. Pan, H. Cai, D.-A. Huang, K.-H. Lee, A. Gaidon, E. Adeli, and J. C. Niebles, “Spatio-temporal graph for video captioning with knowledge distillation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 870–10 879
2020
Later among the works it cites.
D. Ghosal, N. Majumder, A. Gelbukh, R. Mihalcea, and S. Poria, “Cosmic: Commonsense knowledge for emotion identification in conversations,” in EMNLP: Findings , 2020, pp. 2470–2481
2020
Later among the works it cites.
U. Alon and E. Yahav, “On the bottleneck of graph neural networks and its practical implications,” in ICLR , 2020
2020
Later among the works it cites.
P. Papalampidi, F. Keller, and M. Lapata, “Movie summarization via sparse graph construction,” in Thirty-Fifth AAAI Conference on Artificial Intelligence , 2021
2021
Closest in time.
2021
Closest in time.
Q. Ye, X. Shen, Y. Gao, Z. Wang, Q. Bi, P. Li, and G. Yang, “Temporal cue guided video highlight detection with low-rank audio-visual fusion,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 7950–7959
2021
Closest in time.
T. Badamdorj, M. Rochan, Y. Wang, and L. Cheng, “Joint visual and audio learning for video highlight detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 8127–8137
2021
Closest in time.
R. Chen, P. Zhou, W. Wang, N. Chen, P. Peng, X. Sun, and W. Wang, “Pr-net: Preference reasoning for personalized video highlight detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 7980–7989
2021
Closest in time.
2021
Closest in time.
A. Brown, V. Kalogeiton, and A. Zisserman, “Face, body, voice: Video person-clustering with multiple modalities,” in 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) . Los Alamitos, CA, USA: IEEE Computer Society, oct 2021, pp. 3177–3187. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/ICCVW54120.2021.00357
2021
Closest in time.
M. Lee, H. Kwon, J. Shin, W. Lee, B. Jung, and J.-H. Lee, “Transformer-based screenplay summarization using augmented learning representation with dialogue information,” in Proceedings of the 3rd Workshop on Narrative Understanding . Virtual: Association for Computational Linguistics, Jun. 2021, pp. 56–61
2021
Closest in time.
Y. Shen, L. Zhang, K. Xu, and X. Jin, “Autotransition: Learning to recommend video transition effects,” in European Conference on Computer Vision . Springer, 2022, pp. 285–300
2022
Closest in time.
D. M. Argaw, F. C. Heilbron, J.-Y. Lee, M. Woodson, and I. S. Kweon, “The anatomy of video editing: A dataset and benchmark suite for ai-assisted video editing,” in European Conference on Computer Vision . Springer, 2022, pp. 201–218
2022
Closest in time.
A. Pardo, F. C. Heilbron, J. L. Alcázar, A. Thabet, and B. Ghanem, “Moviecuts: A new dataset and benchmark for cut type recognition,” in European Conference on Computer Vision . Springer, 2022, pp. 668–685
2022
Closest in time.
M. Chen, Z. Chu, S. Wiseman, and K. Gimpel, “SummScreen: A dataset for abstractive screenplay summarization,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Dublin, Ireland: Association for Computational Linguistics, May 2022, pp. 8602–8615. [Online]. Available: https://aclanthology.org/2022.acl-long.589
2022
Closest in time.
P. Papalampidi and M. Lapata, “Hierarchical3D adapters for long video-to-text summarization,” in Findings of the Association for Computational Linguistics: EACL 2023 . Dubrovnik, Croatia: Association for Computational Linguistics, May 2023, pp. 1297–1320. [Online]. Available: https://aclanthology.org/2023.findings-eacl.96
2023
Closest in time.