Encoding Feature Maps of CNNs for Action Recognition

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A New Representation of Skeleton Sequences for 3D Action Recognition (2017, methodology): “Then the feature vectors of all time-steps are jointly processed in parallel using multi-task learning, thus to utilize their intrinsic relationships for action recognition.”
Temporal Convolutional Networks for Action Segmentation and Detection (2016, background): “Large-scale Recognition: There has been substantial work on spatiotemporal models for large scale video classification and detection [31, 11, 12, 27, 33, 23, 19].”
Segmental Spatiotemporal CNNs for Fine-Grained Action Segmentation (2016, background): “Large-scale Action Recognition: While recent work has extended CNN models to video [2,22,24,25,26,27,28], often results are only superior when concatenated with IDT features [22,26,27].”
Learning Clip Representations for Skeleton-Based 3D Action Recognition (2018, background): “The activations of each feature map are the local features corresponding to the local regions in the original input frame [55].”
A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences (2017, methodology): “[78] 2015 CNN fc6, HOG/HOF/MBH VGG19 Conv5 79.”