Supplementary Material : Deep Domain Adaptation in Action Space

In the main text, both the proposed approaches have been described in detail. The methods have been extensively evaluated using three sets of multi-domain action datasets. In the main text, due to space constraint, only the results for UO (UCF50 and Olympic Sports) and KMS (KTH, MSR Action II and Sonycam) datasets were presented and discussed and the additional results have been included in the supplementary material. We start with the algorithmic details of Action Modeling on Latent Space (AMLS) approach. In the next section, we describe the Symmetrized KL Divergence measure and then discuss the KMS and UO datasets with few example images. In Section 5, we describe our third dataset collection (HU) comprising five common classes of HMDB51 and UCF50 and present its domain adaptation results. In the next section, we discuss the qualitative analysis of the results for the HU dataset and present some of the negative examples observed in our experiments. Finally, in Section 7, we discuss the hyper-parameters and compare some of the results for their different choices.