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There has been a remarkable progress in learning a model which couldrecognise novel classes with only a few labeled examples in the last few years.Few-shot learning (FSL) for action recognition is a challenging task ofrecognising novel action categories which are represented by few instances inthe training data. We propose a novel variational inference based architecturalframework (HF-AR) for few shot activity recognition. Our framework leveragesvolume-preserving Householder Flow to learn a flexible posterior distributionof the novel classes. This results in better performance as compared tostate-of-the-art few shot approaches for human activity recognition. approachconsists of base model and an adapter model. Our architecture consists of abase model and an adapter model. The base model is trained on seen classes andit computes an embedding that represent the spatial and temporal insightsextracted from the input video, e.g. combination of Resnet-152 and LSTM basedencoder-decoder model. The adapter model applies a series of Householdertransformations to compute a flexible posterior distribution that lends higheraccuracy in the few shot approach. Extensive experiments on three well-knowndatasets: UCF101, HMDB51 and Something-Something-V2, demonstrate similar orbetter performance on 1-shot and 5-shot classification as compared tostate-of-the-art few shot approaches that use only RGB frame sequence as input.To the best of our knowledge, we are the first to explore variational inferencealong with householder transformations to capture the full rank covariancematrix of posterior distribution, for few shot learning in activityrecognition.

Few Shot Activity Recognition Using Variational Inference