A method of machine learning 200 in artificial neural networks (ANN) (Fig. 1, 101) by extracting features and semantic vectors from a video. For the features extracted, performing a feature-based similarity search based on semantic vectors associated with the features 203; generating a joint semantic vector based on the semantic vector and a spatiotemporal vector corresponding to the features and identifying motion in the video based on the joint semantic vector. The neural network may be a convolutional neural network (CNN) and concatenated vectors 205 may be fed into a multilayer perceptron (MLP) neural network 213. The video may comprise RGB pixel values and the similarity search may be a nearest neighbour search. A max pooling operation may be performed to produce the joint semantic vector (Fig. 4, 409). A method to enhance machine learning through generation of a feature tensor is also disclosed, along with an associated computing apparatus for a processing system (Fig. 8, 802).
Representation learning using joint semantic vectors