60 citations · 127 across the 7 of their papers we have counts for
14 papers
Conditional Extreme Value Theory for Open Set Video Domain Adaptation
Zhuoxiao Chen, Yadan Luo, Mahsa Baktashmotlagh
With the advent of media streaming, video action recognition has become progressively important for various applications, yet at the high expense of requiring large-scale data labe…
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot +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…
Domain Adaptative Causality Encoder
Farhad Moghimifar, Gholamreza Haffari, Mahsa Baktashmotlagh
Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefor…
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Yadan Luo, Zi Huang, Hongxu Chen +2
Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions…
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-…