Deep Bilinear Learning for RGB-D Action Recognition

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NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “Speciﬁcally, many of them have been evaluated based on the preliminary version [47] of our dataset, or pre-trained on it for transfer learning for other tasks [43], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79].”
MMTM: Multimodal Transfer Module for CNN Fusion (2019, methodology): “A bilinear pooling block that separately pools input features in modality and time directions is employed in [29].”
Progressive Teacher-Student Learning for Early Action Prediction (2019, methodology): “Since all the actions in SYSU set involve humanobject interactions, we followed the suggestion in [18] and extracted CNN features from the image patches of human body parts.”
Human Action Recognition From Various Data Modalities: A Review (2020, background): “[328] learned the time-varying information across RGB, skeleton, and depth modalities by extracting temporal feature maps from each modality, and then concatenating them along the modality dimension.”