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EnglishMotivated by recent advances in deep domain adaptation, this paper introduces a deep architecture for estimating 3D keypoints when the training (source) and the test (target) images greatly differ in terms of visual appearance (domain shift). Our approach operates by promoting domain distribution alignment in the feature space adopting batch normalization-based techniques. Furthermore, we propose to collect statistics about 3D keypoints positions of the source training data and to use this prior information to constrain predictions on the target domain introducing a loss derived from Multidimensional Scaling. We conduct an extensive experimental evaluation considering three publicly available benchmarks and show that our approach out-performs state-of-the-art domain adaptation methods for 3D keypoints predictions
Structured Domain Adaptation for 3D Keypoint Estimation / Vasconcelos, Levi; Mancini, Massimiliano; Boscaini, Davide; Caputo, Barbara; Ricci, Elisa. - (2019), pp. 57-66. ((Intervento presentato al convegno 2019 International Conference on 3D Vision (3DV) tenutosi a Québec City (Canada) nel 16-19 Sept. 2019.
| Titolo: | Structured Domain Adaptation for 3D Keypoint Estimation |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | Motivated by recent advances in deep domain adaptation, this paper introduces a deep architecture... for estimating 3D keypoints when the training (source) and the test (target) images greatly differ in terms of visual appearance (domain shift). Our approach operates by promoting domain distribution alignment in the feature space adopting batch normalization-based techniques. Furthermore, we propose to collect statistics about 3D keypoints positions of the source training data and to use this prior information to constrain predictions on the target domain introducing a loss derived from Multidimensional Scaling. We conduct an extensive experimental evaluation considering three publicly available benchmarks and show that our approach out-performs state-of-the-art domain adaptation methods for 3D keypoints predictions |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2785962
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