179 citations · 227 across the 20 of their papers we have counts for
9 papers · 2 filters
Manifold Learning Benefits GANs
Yao Ni, Piotr Koniusz, Richard Hartley +1
In this paper, we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-bas…
Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation
Samira Kaviani, Amir Rahimi, Richard Hartley
To obtain 3D annotations, we are restricted to controlled environments or synthetic datasets, leading us to 3D datasets with less generalizability to real-world scenarios. To tackl…
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
Jing Zhang, Yuchao Dai, Mehrtash Harandi +3
Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty. In current aleatoric u…
Invertible Attention
Jiajun Zha, Yiran Zhong, Jing Zhang +2
Attention has been proved to be an efficient mechanism to capture long-range dependencies. However, so far it has not been deployed in invertible networks. This is due to the fact…
One Ring to Rule Them All: a simple solution to multi-view 3D-Reconstruction of shapes with unknown BRDF via a small Recurrent ResNet
Ziang Cheng, Hongdong Li, Richard Hartley +2
This paper proposes a simple method which solves an open problem of multi-view 3D-Reconstruction for objects with unknown and generic surface materials, imaged by a freely moving c…
Learning Optical Flow from a Few Matches
Shihao Jiang, Yao Lu, Hongdong Li +1
State-of-the-art neural network models for optical flow estimation require a dense correlation volume at high resolutions for representing per-pixel displacement. Although the dens…