Handcrafted vs. learned representations for human action recognition
From handcrafted to learned representations for human action recognition: A survey
Towards optimal VLAD for human action recognition from still images
Robust geometric ℓp-norm feature pooling for image classification and action recognition
Action recognition by joint learning
Dynamic texture recognition with video set based collaborative representation
3D-based Deep Convolutional Neural Network for action recognition with depth sequences
Deep and fast: Deep learning hashing with semi-supervised graph construction
Cross-view action recognition by cross-domain learning
Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization
Dual many-to-one-encoder-based transfer learning for cross-dataset human action recognition
Statistical adaptive metric learning in visual action feature set recognition
Using the conflict in Dempster–Shafer evidence theory as a rejection criterion in classifier output combination for 3D human action recognition