  Despite the fact that notable improvements have been made recently in the
field of feature extraction and classification, human action recognition is
still challenging, especially in images, in which, unlike videos, there is no
motion. Thus, the methods proposed for recognizing human actions in videos
cannot be applied to still images. A big challenge in action recognition in
still images is the lack of large enough datasets, which is problematic for
training deep Convolutional Neural Networks (CNNs) due to the overfitting
issue. In this paper, by taking advantage of pre-trained CNNs, we employ the
transfer learning technique to tackle the lack of massive labeled action
recognition datasets. Furthermore, since the last layer of the CNN has
class-specific information, we apply an attention mechanism on the output
feature maps of the CNN to extract more discriminative and powerful features
for classification of human actions. Moreover, we use eight different
pre-trained CNNs in our framework and investigate their performance on Stanford
40 dataset. Finally, we propose using the Ensemble Learning technique to
enhance the overall accuracy of action classification by combining the
predictions of multiple models. The best setting of our method is able to
achieve 93.17$\%$ accuracy on the Stanford 40 dataset.
