Semi-Supervised Few-Shot Classification with Deep Invertible Hybrid Models

no code implementations • 22 May 2021 • Yusuke Ohtsubo, Tetsu Matsukawa, Einoshin Suzuki

In this paper, we propose a deep invertible hybrid model which integrates discriminative and generative learning at a latent space level for semi-supervised few-shot classification.

Classification Few-Shot Learning

Hierarchical Gaussian Descriptors with Application to Person Re-Identification

no code implementations • 14 Jun 2017 • Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato

To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters.

Image Classification Person Re-Identification

Hierarchical Gaussian Descriptor for Person Re-Identification

no code implementations • CVPR 2016 • Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato

In both steps, unlike the hierarchical covariance descriptor, the proposed descriptor can model both the mean and the covariance information of pixel features properly.

Image Classification Person Re-Identification