316 citations · 526 across the 12 of their papers we have counts for
13 papers
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…
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…
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…
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,…
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…
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…
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors
Chamara Saroj Weerasekera, Thanuja Dharmasiri, Ravi Garg +2
We present ``just-in-time reconstruction" as real-time image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In part…
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera +3
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recen…
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction
Chamara Saroj Weerasekera, Ravi Garg, Yasir Latif +1
Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images. These f…
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
Yasir Latif, Ravi Garg, Michael Milford +1
Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception ta…
Adaptive Low-Rank Kernel Subspace Clustering
Pan Ji, Ian Reid, Ravi Garg +2
In this paper, we present a kernel subspace clustering method that can handle non-linear models. In contrast to recent kernel subspace clustering methods which use predefined kerne…
Non-linear Dimensionality Regularizer for Solving Inverse Problems
Ravi Garg, Anders Eriksson, Ian Reid
Consider an ill-posed inverse problem of estimating causal factors from observations, one of which is known to lie near some (un- known) low-dimensional, non-linear manifold expres…
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Ravi Garg, Vijay Kumar BG, Gustavo Carneiro +1
A significant weakness of most current deep Convolutional Neural Networks is the need to train them using vast amounts of manu- ally labelled data. In this work we propose a unsupe…