64 citations · 150 across the 7 of their papers we have counts for
6 papers
The Edge of Disaster: A Battle Between Autonomous Racing and Safety
Matthew Howe, James Bockman, Adrian Orenstein +3
Autonomous racing represents a uniquely challenging control environment where agents must act while on the limits of a vehicle's capability in order to set competitive lap times. T…
Deep Learning Features at Scale for Visual Place Recognition
Zetao Chen, Adam Jacobson, Niko Sunderhauf +5
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using gen…
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
Dong Gong, Jie Yang, Lingqiao Liu +5
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior,…
Attend in groups: a weakly-supervised deep learning framework for learning from web data
Bohan Zhuang, Lingqiao Liu, Yao Li +2
Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web im…
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Guosheng Lin, Anton Milan, Chunhua Shen +1
Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification probl…
A Framework for the Volumetric Integration of Depth Images
Victor Adrian Prisacariu, Olaf Kähler, Ming Ming Cheng +5
Volumetric models have become a popular representation for 3D scenes in recent years. One of the breakthroughs leading to their popularity was KinectFusion, where the focus is on 3…