Global Regularizer and Temporal-Aware Cross-Entropy for Skeleton-Based Early Action Recognition
By Qiuhong Ke, Jun Liu, Mohammed Bennamoun, Hossein Rahmani, Senjian An, Ferdous Sohel, Farid Boussaïd. 2018
14 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition, Signal Processing
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Citations in the corpus
- 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].”
- Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Thus many recent skeleton-based HAR works [175], [176], [177] used the skeleton data acquired with pose estimation and tracking algorithms using depth maps (e.”