Unsupervised learning of landmarks by descriptor vector exchange
Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision. However, this method does not explicitly guarantee that the learned landmarks are consistent with changes between different...
Main Authors: | Thewlis, J, Albanie, S, Bilen, H, Vedaldi, A |
---|---|
Formato: | Conference item |
Idioma: | English |
Publicado em: |
IEEE
2020
|
Registos relacionados
-
Unsupervised learning of object landmarks by factorized spatial embeddings
Por: Thewlis, J, et al.
Publicado em: (2017) -
Modelling and unsupervised learning of symmetric deformable object categories
Por: Thewlis, J, et al.
Publicado em: (2018) -
Unsupervised learning of object frames by dense equivariant image labelling
Por: Thewlis, J, et al.
Publicado em: (2017) -
Unsupervised learning of object landmarks through conditional image generation
Por: Jakab, T, et al.
Publicado em: (2018) -
Learning grimaces by watching TV
Por: Albanie, S, et al.
Publicado em: (2016)