Shape Consistent 2D Keypoint Estimation under Domain Shift
- URL: http://arxiv.org/abs/2008.01589v1
- Date: Tue, 4 Aug 2020 14:32:06 GMT
- Title: Shape Consistent 2D Keypoint Estimation under Domain Shift
- Authors: Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini, Samuel
Rota Bulo, Barbara Caputo, Elisa Ricci
- Abstract summary: We present a novel deep adaptation framework for estimating keypoints under domain shift.
Our method seamlessly combines three different components: feature alignment, adversarial training and self-supervision.
Our approach outperforms state-of-the-art domain adaptation methods in the 2D keypoint prediction task.
- Score: 35.15266729401601
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent unsupervised domain adaptation methods based on deep architectures
have shown remarkable performance not only in traditional classification tasks
but also in more complex problems involving structured predictions (e.g.
semantic segmentation, depth estimation). Following this trend, in this paper
we present a novel deep adaptation framework for estimating keypoints under
domain shift}, i.e. when the training (source) and the test (target) images
significantly differ in terms of visual appearance. Our method seamlessly
combines three different components: feature alignment, adversarial training
and self-supervision. Specifically, our deep architecture leverages from
domain-specific distribution alignment layers to perform target adaptation at
the feature level. Furthermore, a novel loss is proposed which combines an
adversarial term for ensuring aligned predictions in the output space and a
geometric consistency term which guarantees coherent predictions between a
target sample and its perturbed version. Our extensive experimental evaluation
conducted on three publicly available benchmarks shows that our approach
outperforms state-of-the-art domain adaptation methods in the 2D keypoint
prediction task.
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