Multi-Modal Domain Adaptation for Fine-Grained Action Recognition

Abstract
Fine-grained 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...
Paper Details
Title
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
Published Date
Jan 27, 2020
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