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Detection of video shot transition is a crucial pre-processing step in video analysis.
Video shot cut detection using adaptive thresholding
Yusoff, Y., Christmas, W.J., Kittler, J.: · 2000
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A unified shot boundary detection framework based on graph partition model
Yuan, J., Li, J., Lin, F., Zhang, B.: · 2005
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A formal study of shot boundary detection
Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F., Zhang, B.: · 2007
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At&t research at trecvid 2007
Liu, Z., Gibbon, D., Zavesky, E., Shahraray, B., Haffner, P.: · 2007
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University of marburg at trecvid 2007: Shot boundary detection and high level feature extraction
Mühling, M., Ewerth, R., Stadelmann, T., Zöfel, C., Shi, B., Freisleben, B.: · 2007
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Shot boundary detection at trecvid 2007
Kawai, Y., Sumiyoshi, H., Yagi, N.: · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Fast video shot boundary detection based on svd and pattern matching
Lu, Z.M., Shi, Y.: · 2013
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Learning fine-grained image similarity with deep ranking
Wang, J., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y., et al.: · 2014
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Walsh–hadamard transform kernel-based feature vector for shot boundary detection
Domnic, S.: · 2014
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Fast shot segmentation combining global and local visual descriptors
Apostolidis, E., Mezaris, V.: · 2014
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Learning to compare image patches via convolutional neural networks
Zagoruyko, S., Komodakis, N.: · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Shot and scene detection via hierarchical clustering for re-using broadcast video
Baraldi, L., Grana, C., Cucchiara, R.: · 2015
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Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
Hassanien, A., Elgharib, M., Selim, A., Hefeeda, M., Matusik, W.: · 2017
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Ridiculously fast shot boundary detection with fully convolutional neural networks
Gygli, M.: · 2017
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The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
Carreira, J., Zisserman, A.: · 2017
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Learning spatio-temporal representation with pseudo-3d residual networks
Qiu, Z., Yao, T., Mei, T.: · 2017
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Daps: Deep action proposals for action understanding
Escorcia, V., Heilbron, F.C., Niebles, J.C., Ghanem, B.: · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: · 2016
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To click or not to click: Automatic selection of beautiful thumbnails from videos
Song, Y., Redi, M., Vallmitjana, J., Jaimes, A.: · 2016
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R-c3d: Region convolutional 3d network for temporal activity detection
Xu, H., Das, A., Saenko, K.: · 2017
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Single shot temporal action detection
Lin, T., Zhao, X., Shou, Z.: · 2017
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Temporal action detection with structured segment networks
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: · 2017
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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
Hara, K., Kataoka, H., Satoh, Y.: · 2017
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