68 citations · 216 across the 24 of their papers we have counts for
5 papers · 2 filters
Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo, Zijian Wang, Zi Huang +1
Domain shift is a fundamental problem in visual recognition which typically arises when the source and target data follow different distributions. The existing domain adaptation ap…
A Simple and Scalable Shape Representation for 3D Reconstruction
Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh +1
Deep learning applied to the reconstruction of 3D shapes has seen growing interest. A popular approach to 3D reconstruction and generation in recent years has been the CNN encoder-…
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Implicitly Defined Layers in Neural Networks
Qianggong Zhang, Yanyang Gu, Michalkiewicz Mateusz +2
In conventional formulations of multilayer feedforward neural networks, the individual layers are customarily defined by explicit functions. In this paper we demonstrate that defin…
Learning landmark guided embeddings for animal re-identification
Olga Moskvyak, Frederic Maire, Feras Dayoub +1
Re-identification of individual animals in images can be ambiguous due to subtle variations in body markings between different individuals and no constraints on the poses of animal…