activity
20162022
most citedDF-VO: What Should Be Learnt for Visual Odometry?

27 citations · 48 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV20224 cited

Retrieval Augmented Classification for Long-Tail Visual Recognition

Alexander Long, Wei Yin, Thalaiyasingam Ajanthan +6

We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module. RAC consists of a…

cs.CV202127 cited

DF-VO: What Should Be Learnt for Visual Odometry?

Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian +2

Multi-view geometry-based methods dominate the last few decades in monocular Visual Odometry for their superior performance, while they have been vulnerable to dynamic and low-text…

cs.LG20193 cited

Improved Visual Localization via Graph Smoothing

Carlos Lassance, Yasir Latif, Ravi Garg +2

Vision based localization is the problem of inferring the pose of the camera given a single image. One solution to this problem is to learn a deep neural network to infer the pose…

cs.CV20197 cited

Non-Parametric Priors For Generative Adversarial Networks

Rajhans Singh, Pavan Turaga, Suren Jayasuriya +2

The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However,…

cs.CV20197 cited

Self-supervised Learning for Single View Depth and Surface Normal Estimation

Huangying Zhan, Chamara Saroj Weerasekera, Ravi Garg +1

In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single ima…

cs.CV2018

Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette

Kejie Li, Ravi Garg, Ming Cai +1

3D shape reconstruction from a single image is a highly ill-posed problem. Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image…