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An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images
2022
Neural computing & applications (Print)
AbstractStill image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study,
doi:10.1007/s00521-022-06947-6
fatcat:tqkjrj2l2vflvcuebl2hcmihsy