Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach

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Video Representation Learning Using Discriminative Pooling (2018, methodology): “present a compositional model in [48] for video event detection using a multiple kernel learning based latent SVM.”
A Robust and Efficient Video Representation for Action Recognition (2015, unknown): “Vahdat et al (2013) achieved 15.7% by using multiple kernel learning to combine different features, and latent variables to infer the relevant portions of the videos.”
DISCOVER: Discovering Important Segments for Classification of Video Events and Recounting (2014, background): “To find the discriminative parts of videos, [24] learns kernelized latent SVMs with no temporal constraints.”
Semantic Pooling for Complex Event Analysis in Untrimmed Videos (2016, background): “…to our work here, a number of previous works [2], [32], [33] have considered aggregating complementary features at the video level while others considered combining multiple statistical classiﬁers [34], [35], [36], including those trying to model the temporal information explicitly [37], [38] .”