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A New Large Scale Dynamic Texture Dataset with Application to ConvNet Understanding.

, and . ECCV (14), volume 11218 of Lecture Notes in Computer Science, page 334-351. Springer, (2018)

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What Have We Learned From Deep Representations for Action Recognition?, , , and . CVPR, page 7844-7853. Computer Vision Foundation / IEEE Computer Society, (2018)Spatiotemporal Residual Networks for Video Action Recognition., , and . NIPS, page 3468-3476. (2016)Detecting Motion Patterns via Direction Maps with Application to Surveillance., , and . WACV/MOTION, page 202-209. IEEE Computer Society, (2005)Bags of Spacetime Energies for Dynamic Scene Recognition., , and . CVPR, page 2681-2688. IEEE Computer Society, (2014)Efficient action spotting based on a spacetime oriented structure representation., , , and . CVPR, page 1990-1997. IEEE Computer Society, (2010)Detecting Binocular Half-Occlusions: Empirical Comparisons of Four Approaches., and . CVPR, page 2466-2473. IEEE Computer Society, (2000)Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition., , , and . CoRR, (2024)The Structure of Multiplicative Motions in Natural Imagery., and . IEEE Trans. Pattern Anal. Mach. Intell., 32 (7): 1310-1316 (2010)What Do We Understand About Convolutional Networks?, and . (2018)cite arxiv:1803.08834.Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability., and . CoRR, (2023)