Wei Jiang
University of Victoria
13 Papers
16 Citations
Wei Jiang is an academic researcher from University of Victoria. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 6, co-authored 12 publications. Previous affiliations of Wei Jiang include University of British Columbia.
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Papers
•Proceedings Article
COTR: Correspondence Transformer for Matching Across Images
Wei Jiang,Eduard Trulls,Jan Hosang,Andrea Tagliasacchi,Kwang Moo Yi +4 more
- 25 Mar 2021
TL;DR: In this article, the authors propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
Weiwei Sun,Wei Jiang,Eduard Trulls,Andrea Tagliasacchi,Kwang Moo Yi +4 more
- 14 Jun 2020
TL;DR: In this article, the authors propose an attention-based normalization of the feature maps of a permutation-equivariant network to find the essential data points in high-dimensional space to solve a given task.
DeRF: Decomposed Radiance Fields
Daniel Rebain,Wei Jiang,Soroosh Yazdani,Ke Li,Kwang Moo Yi,Andrea Tagliasacchi +5 more
- 01 Jun 2021
TL;DR: In this article, the authors propose a technique based on spatial decomposition capable of mitigating the limitations of neural networks in real-world scenarios, which is provably compatible with the Painter's algorithm for GPU-friendly rendering.
•Posted Content
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
TL;DR: This paper shows how to normalize the feature maps with weights that are estimated within the network, excluding outliers from this normalization, and uses this mechanism to leverage two types of attention: local and global – by combining them, the method is able to find the essential data points in high-dimensional space in order to solve a given task.
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Optimizing Through Learned Errors for Accurate Sports Field Registration
Wei Jiang,Juan Camilo Gamboa Higuera,Baptiste Angles,Weiwei Sun,Mehrsan Javan,Kwang Moo Yi +5 more
- 01 Mar 2020
TL;DR: In this article, an optimization-based framework is proposed to register sports field templates onto broadcast videos by training a deep network that regresses the registration error, and then finding the registration parameters that minimize the regressed error.
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