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Many real-world video analysis applications require the ability to identify domain-specific events in video, such as interviews and commercials in TV news broadcasts, or action sequences in film.
Maintaining knowledge about temporal intervals
J. F. Allen · 1983
Earlier work this paper cites.
Ovid: design and implementation of a video-object database system
E. Oomoto and K. Tanaka · 1993
Earlier work this paper cites.
Temporal specialization and generalization
C. S. Jensen and R. Snodgrass · 1994
Earlier work this paper cites.
A visual query language for identifying temporal trends in video data
S. Hibino and E. A. Rundensteiner · 1995
Earlier work this paper cites.
The advanced video information system: data structures and query processing
S. Adalı, K. S. Candan, S.-S. Chen, K. Erol, and V. Subrahmanian · 1996
Earlier work this paper cites.
A content-based query language for video databases
T. C. T. Kuo and A. L. P. Chen · 1996
Earlier work this paper cites.
Qualitative spatial representation and reasoning with the region connection calculus
A. G. Cohn, B. Bennett, J. Gooday, and N. M. Gotts · 1997
Earlier work this paper cites.
Modeling of moving objects in a video database
J. Z. Li, M. T. Özsu, and D. Szafron · 1997
Earlier work this paper cites.
Spatio-temporal querying in video databases
M. Köprülü, N. K. Cicekli, and A. Yazici · 2002
Earlier work this paper cites.
A framework for aligning and indexing movies with their script
R. Ronfard and T. T. Thuong · 2003
Earlier work this paper cites.
Rule-based spatiotemporal query processing for video databases
M. E. Dönderler, O. Ulusoy, and U. Güdükbay · 2004
Earlier work this paper cites.
Automatic generation of movie trailers using ontologies
C. Brachmann, H. I. Chunpir, S. Gennies, B. Haller, T. Hermes, O. Herzog, A. Jacobs, P. Kehl, A. P. Mochtarram, D. Möhlmann, et al · 2007
Earlier work this paper cites.
Indexing of fictional video content for event detection and summarisation
B. Lehane, N. E. O’Connor, H. Lee, and A. F. Smeaton · 2007
Earlier work this paper cites.
Quicker, faster, darker: Changes in Hollywood film over 75 years
J. E. Cutting, K. L. Brunick, D. J. E., C. Iricinschi, and A. Candan · 2011
Earlier work this paper cites.
Designing an interface for a digital movie browsing system in the film studies domain
N. Mohamad Ali, A. F. Smeaton, and H. Lee · 2011
Earlier work this paper cites.
Low-level features of film: What they are and why we would be lost without them
K. L. Brunick, J. E. Cutting, and D. J. E · 2013
Earlier work this paper cites.
Attribute-based classification for zero-shot visual object categorization
C. H. Lampert, H. Nickisch, and S. Harmeling · 2013
Earlier work this paper cites.
Zero-shot learning through cross-modal transfer
R. Socher, M. Ganjoo, C. D. Manning, and A. Ng · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Trill: A high-performance incremental query processor for diverse analytics
B. Chandramouli, J. Goldstein, M. Barnett, R. DeLine, D. Fisher, J. Platt, J. Terwilliger, J. Wernsing, and R. DeLine · 2015
Earlier work this paper cites.
The framing of characters in popular movies
J. E. Cutting · 2015
Earlier work this paper cites.
Shot durations, shot classes, and the increased pace of popular movies
J. E. Cutting and A. Candan · 2015
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Objects2action: Classifying and localizing actions without any video example
M. Jain, J. C. van Gemert, T. Mensink, and C. G. Snoek · 2015
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Sceneskim: Searching and browsing movies using synchronized captions, scripts and plot summaries
A. Pavel, D. B. Goldman, B. Hartmann, and M. Agrawala · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
Fine-grained retrieval of sports plays using tree-based alignment of trajectories
L. Sha, P. Lucey, S. Zheng, T. Kim, Y. Yue, and S. Sridharan · 2017
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Harnessing ai for augmenting creativity: Application to movie trailer creation
J. R. Smith, D. Joshi, B. Huet, W. Hsu, and J. Cota · 2017
Later among the works it cites.
Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
Later among the works it cites.
Inferring generative model structure with static analysis
P. Varma, B. D. He, P. Bajaj, N. Khandwala, I. Banerjee, D. Rubin, and C. Ré · 2017
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Analyzing Elements of Style in Annotated Film Clips
H.-Y. Wu, Q. Galvane, C. Lino, and M. Christie · 2017
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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
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The evolution of pace in popular movies
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Facial expression, size, and clutter: Inferences from movie structure to emotion judgments and back
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Introduction to Apache Flink: Stream Processing for Real Time and Beyond
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You lead, we exceed: Labor-free video concept learning by jointly exploiting web videos and images
C. Gan, T. Yao, K. Yang, Y. Yang, and T. Mei · 2016
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Continuous analytics on graph data streams using wso2 complex event processor
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Data programming: Creating large training sets, quickly
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Blazeit: Fast exploratory video queries using neural networks
D. Kang, P. Bailis, and M. Zaharia · 2018
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Model assertions for debugging machine learning
D. Kang, D. Raghavan, P. Bailis, and M. Zaharia · 2018
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Scanner: Efficient video analysis at scale
A. Poms, W. Crichton, P. Hanrahan, and K. Fatahalian · 2018
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Mythbusting set-pieces in soccer
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Snorkel: Rapid training data creation with weak supervision
A. Ratner, S. H. Bach, H. Ehrenberg, J. Fries, S. Wu, and C. Ré · 2018
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Fast video shot transition localization with deep structured models
S. Tang, L. Feng, Z. Kuang, Y. Chen, and W. Zhang · 2018
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Thinking like a director: Film editing patterns for virtual cinematographic storytelling
H.-Y. Wu, F. Palù, R. Ranon, and M. Christie · 2018
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One-shot action localization by learning sequence matching network
H. Yang, X. He, and F. Porikli · 2018
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