no code implementations • 12 May 2023 • Myung-Cheol Roh, Pyoung-gang Lim, Jongju Shin
Previous works have been trying to deal with the problem only in training domain, however it can cause much serious problem if the mistakes are in gallery data of face identification.
1 code implementation • ECCV 2020 • Yonghyun Kim, Wonpyo Park, Jongju Shin
Moreover, we propose a novel compensation method to increase the number of referenced instances in the training stage.
3 code implementations • CVPR 2020 • Yonghyun Kim, Wonpyo Park, Myung-Cheol Roh, Jongju Shin
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.
no code implementations • 4 Jul 2018 • Minseong Kim, Jongju Shin, Myung-Cheol Roh, Hyun-Chul Choi
Although the pre-trained network is used to generate responses of receptive fields effective for representing style and content of image, it is not optimized for image style transfer but rather for image classification.
no code implementations • 19 Jul 2017 • Vishnu Naresh Boddeti, Myung-Cheol Roh, Jongju Shin, Takaharu Oguri, Takeo Kanade
To account for partial occlusions we introduce, Robust Constrained Local Models, that comprises of a deformable shape and local landmark appearance model and reasons over binary occlusion labels.