Adaptive RNN Tree for Large-Scale Human Action Recognition
By Wenbo Li, Longyin Wen, Ming-Ching Chang, Ser-Nam Lim, Siwei Lyu. 2017
104 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition
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Citations in the corpus
- Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data (2021, background): “Various skeleton-based action recognition approaches [5, 29, 13, 17, 20, 18, 19, 30, 42, 15, 27, 31, 39, 14] have been proposed.”
- Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (2019, unknown): “racy (%). Method Year CS CV HBRNN-L [6] 2015 59.1 64.0 Part-aware LSTM [31] 2016 62.9 70.3 ST-LSTM + Trust Gate [22] 2016 69.2 77.7 STA-LSTM [34] 2017 73.4 81.2 GCA-LSTM [24] 2017 74.4 82.8 URNN-2L-T [20] 2017 74.6 83.2 Clips+CNN+MTLN [15] 2017 79.6 84.8 VA-LSTM [51] 2017 79.4 87.6 ElAtt-GRU[53] 201”
- View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition (2018, methodology): “To be view-invariant, the commonly used strategies are to perform pre-processing of skeletons [6], [17], [25], [27], [29], [38], [42], [45], [55], [62].”