Recognizing unseen actions in a domain adapted embedding space
With the sustaining bloom of multimedia data, Zero shot Learning (ZSL) techniques have attracted much attention in recent years for its ability to train learning models that can handle "unseen" categories. Existing ZSL algorithms mainly take advantages of attribute based semantic space and only focus on static image data. Besides, most ZSL studies merely consider the semantic embedded labels and fail to address domain shift problem. In this paper, we purpose a deep two output model for video ZSL and action recognition tasks by computing both spatial and temporal features from video contents through distinct Convolutional Neural Networks (CNNs) and training a Multi layer Perceptron (MLP) upon extracted features to map videos to semantic embedding word vectors. Moreover, we introduce a domain adaptation strategy named "ConSSEV" by combining outputs from two distinct output layers of our MLP to improve the results of zero shot learning. Our experiments on UCF101 dataset demonstrate the purposed model has more advantages associated with more complex video embedding schemes, and outperforms the state of the art zero shot learning techniques.
2016 IEEE International Conference on Image Processing (ICIP)