Unsupervised domain adaptation (UDA) methods have become very popular in computer vision. However, while several techniques have been proposed for images, much less attention has been devoted to videos. This paper introduces a novel UDA approach for action recognition from videos, inspired by recent literature on contrastive learning. In particular, we propose a novel two-headed deep architecture that simultaneously adopts cross-entropy and contrastive losses from different network branches to robustly learn a target classifier. Moreover, this work introduces a novel large-scale UDA dataset, Mixamo→Kinetics, which, to the best of our knowledge, is the first dataset that considers the domain shift arising when transferring knowledge from synthetic to real video sequences. Our extensive experimental evaluation conducted on three publicly available benchmarks and on our new Mixamo→Kinetics dataset demonstrate the effectiveness of our approach, which outperforms the current state-of-the-art methods. Code is available at https://github.com/vturrisi/CO2A.

Dual-Head Contrastive Domain Adaptation for Video Action Recognition

Zara, Giacomo;Ricci, Elisa
2022-01-01

Abstract

Unsupervised domain adaptation (UDA) methods have become very popular in computer vision. However, while several techniques have been proposed for images, much less attention has been devoted to videos. This paper introduces a novel UDA approach for action recognition from videos, inspired by recent literature on contrastive learning. In particular, we propose a novel two-headed deep architecture that simultaneously adopts cross-entropy and contrastive losses from different network branches to robustly learn a target classifier. Moreover, this work introduces a novel large-scale UDA dataset, Mixamo→Kinetics, which, to the best of our knowledge, is the first dataset that considers the domain shift arising when transferring knowledge from synthetic to real video sequences. Our extensive experimental evaluation conducted on three publicly available benchmarks and on our new Mixamo→Kinetics dataset demonstrate the effectiveness of our approach, which outperforms the current state-of-the-art methods. Code is available at https://github.com/vturrisi/CO2A.
978-1-6654-0915-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/335800
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