Human Action Recognition across Datasets by Foreground-Weighted Histogram Decomposition

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RGB-D-based action recognition datasets: A survey (2016, methodology): “The cross-dataset scheme has already been adopted on some algorithms for action recognition in 2D videos [110,111], however, to our best knowledge, there is no report of its usage on RGB-D video datasets.”
RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “Even though some works have studied this topic [11, 123, 171], they are all based on hand-crafted features, and the results are far from satisfactory due to the large distribution variances between different datasets, including different scenarios, different modalities, different views, different pe”
Deep Domain Adaptation in Action Space (2018, methodology): “For the UCF50 dataset, we use 70%- 30% train-test split suggested in [27], which results into 432 − 168 train/test action videos for training and testing.”
Temporal Attentive Alignment for Large-Scale Video Domain Adaptation (2019, methodology): “[43] improves the domain generalizability by decreasing the effect of the background.”