Cited by Lee Sonogan
Abstract by Tianjiao Li, Qiuhong Ke, Hossein Rahmani, Rui En Ho, Henghui Ding, Jun Liu;
We address continual action recognition from skeleton sequence, which aims to learn a recognition model over time from a continuous stream of skeleton data. This task is very important in changing environment. Due to catastrophic forgetting problems of deep neural networks and large discrepancies between the previously learned and current new human actions from different categories, the neural networks may “forget” old actions, when learning new actions. This makes online continual action recognition a challenging task. We observe that although different human actions may vary to a large extent as a whole, their local body parts could share similar features. Therefore, we propose an Elastic Semantic Network (Else-Net) to learn new actions by decomposing human bodies into several semantic body parts. For each body part, the proposed Else-Net constructs a semantic pathway using several elastic cells learned with old actions, or explores new cells to store new knowledge.
Publication: International Conference on Computer Vision (Peer-Reviewed Journal)
Pub Date: 2021
Keywords: Semantics, Elastic, Skeleton Data, Continual Action
https://openaccess.thecvf.com/content/ICCV2021/html/Li_Else-Net_Elastic_Semantic_Network_for_Continual_Action_Recognition_From_Skeleton_ICCV_2021_paper.html (Plenty more sections and references in this research article)