Conditional Extreme Value Theory for Open Set Video Domain Adaptation

• URL: http://arxiv.org/abs/2109.00522v1
• Date: Wed, 1 Sep 2021 10:51:50 GMT
• Title: Conditional Extreme Value Theory for Open Set Video Domain Adaptation
• Authors: Zhuoxiao Chen, Yadan Luo, Mahsa Baktashmotlagh
• Abstract summary: We propose an open-set video domain adaptation approach to mitigate the domain discrepancy between the source and target data. To alleviate the negative transfer issue, weights computed by the distance from the sample entropy to the threshold are leveraged in adversarial learning. The proposed method has been thoroughly evaluated on both small-scale and large-scale cross-domain video datasets.
• Score: 17.474956295874797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advent of media streaming, video action recognition has become progressively important for various applications, yet at the high expense of requiring large-scale data labelling. To overcome the problem of expensive data labelling, domain adaptation techniques have been proposed that transfers knowledge from fully labelled data (i.e., source domain) to unlabelled data (i.e., target domain). The majority of video domain adaptation algorithms are proposed for closed-set scenarios in which all the classes are shared among the domains. In this work, we propose an open-set video domain adaptation approach to mitigate the domain discrepancy between the source and target data, allowing the target data to contain additional classes that do not belong to the source domain. Different from previous works, which only focus on improving accuracy for shared classes, we aim to jointly enhance the alignment of shared classes and recognition of unknown samples. Towards this goal, class-conditional extreme value theory is applied to enhance the unknown recognition. Specifically, the entropy values of target samples are modelled as generalised extreme value distributions, which allows separating unknown samples lying in the tail of the distribution. To alleviate the negative transfer issue, weights computed by the distance from the sample entropy to the threshold are leveraged in adversarial learning in the sense that confident source and target samples are aligned, and unconfident samples are pushed away. The proposed method has been thoroughly evaluated on both small-scale and large-scale cross-domain video datasets and achieved the state-of-the-art performance.

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