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%.

DomainCamera Adaptation for Unsupervised Person ReIdentification Although supervised person reidentification (ReID) performance has been significantly improved in recent years, it is still a challenge for unsupervised person ReIddue to its absence of labels across disjoint camera views. On the other hand, ReIdmodels trained on source domain usually offer poor performance when they are tested on target domain due to interdomain bias e.g. different classes and intradomain difference e.g camera variance. To overcome this problem, given a labeled source training domain and an unlabeled target training domain, we propose an unsupervised transfer method, DomainCamera Adaptation model, to generate a pseudo target domain by bridging interdomain bias and intradomain difference. The idea is to fill the absence of labels in target domain by transferring labeled images of source domain to target domain across cameras. Then we propose a crossdomain classification loss to extract discriminative representation across domains. The intuition is to think of unsupervised learning as semisupervised learning in target domain. We evaluate our deep model on Market1501 and DukeMTMCreID and the results show our model outperforms the stateofart unsupervised ReID methods by large margins.