An Unsupervised SequencetoSequence Autoencoder Based Human Action Scoring Model Developing a model for the task of assessing quality of human action is a key research area in computer vision. The quality assessment task has been posed as a supervised regression problem, where models have been trained to predict score, given action representation features. However, human proficiency levels can widely vary and so do their scores. Providing all such performance variations and their respective scores is an expensive solution as it requires a domain expert to annotate many videos. The question arises  Can we exploit the variations of the performances from that of expert and map the variations to their respective scores? To this end, we introduce a novel sequencetosequence autoencoderbased scoring model which learns the representation from only expert performances and judges an unknown performance based on how well it can be regenerated from the learned model. We evaluated our model in predicting scores of a complex Sun Salutation action sequence, and demonstrate that our model gives remarkable prediction accuracy compared to the baselines.

MultiModal Domain Adaptation for FineGrained Action Recognition. Finegrained action recognition datasets exhibit environmental bias, where multiple video sequences are captured from a limited number of environments. Training a model in one environment and deploying in another results in a drop in performance due to an unavoidable domain shift. Unsupervised Domain Adaptation (UDA) approaches have frequently utilised adversarial training between the source and target domains. However, these approaches have not explored the multimodal nature of video within each domain. In this work we exploit the correspondence of modalities as a selfsupervised alignment approach for UDA in addition to adversarial alignment.  :92,test our approach on three kitchens from our largescale dataset, EPICKitchens, using two modalities commonly employed for action recognition: RGB and Optical Flow. We show that multimodal selfsupervision alone improves the performance over sourceonly training by 2.4% on average. We then combine adversarial training with multimodal selfsupervision, showing that our approach outperforms other UDA methods by 3%.