Aug 7, 2017 · This paper addresses both of those challenges, through an image to video feature-level domain adaptation approach, to learn discriminative video ...
The framework utilizes large-scale unlabeled video data to re- duce the gap between different domains while transferring discriminative knowledge from large- ...
The framework utilizes large-scale unlabeled video data to re- duce the gap between different domains while transferring discriminative knowledge from large- ...
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face ...
We propose an unsupervised domain adaptation methodfor video face recognition using large-scale unlabeled videos andlabeled still images. To help bridge the ...
(2017) propose to achieve adversarial UDA for video face recognition. ... ... Annotation of all video frames is labor-intensive and time-consuming for different ...
Aug 7, 2017 · This paper addresses both of those challenges, through an image to video feature-level domain adaptation approach, to learn discriminative video ...
In this section, we present detailed network architecture as well as implementation details, for reproducible research. The network architecture for the ...
People also ask
What is an example of unsupervised domain adaptation?
What is the difference between supervised and unsupervised domain adaptation?
Which domain is face recognition an example of?
What is domain adaptation in image processing?
An application of Unsupervised Domain Adaptation (UDA) is unconstrained face recognition ... image features from the unlabeled target domain training set.
The unsupervised domain adaptation (UDA) task is therefore introduced where models are adapted from the labeled source domain toward the unlabeled target domain ...
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