1 code implementation • 7 Nov 2018 • Ahmad Khaliq, Shoaib Ehsan, Zetao Chen, Michael Milford, Klaus McDonald-Maier
This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes.
no code implementations • CVPR 2018 • Michel Keller, Zetao Chen, Fabiola Maffra, Patrik Schmuck, Margarita Chli
Research on learning suitable feature descriptors for Computer Vision has recently shifted to deep learning where the biggest challenge lies with the formulation of appropriate loss functions, especially since the descriptors to be learned are not known at training time.
1 code implementation • 11 Sep 2017 • Inkyu Sa, Zetao Chen, Marija Popovic, Raghav Khanna, Frank Liebisch, Juan Nieto, Roland Siegwart
In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV).
no code implementations • 24 Jul 2017 • ZongYuan Ge, Sergey Demyanov, Zetao Chen, Rahil Garnavi
We present a conceptually new and flexible method for multi-class open set classification.
no code implementations • 18 Jan 2017 • Zetao Chen, Adam Jacobson, Niko Sunderhauf, Ben Upcroft, Lingqiao Liu, Chunhua Shen, Ian Reid, Michael Milford
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 generic features from networks that were trained for other types of recognition tasks.
no code implementations • 6 Nov 2014 • Zetao Chen, Obadiah Lam, Adam Jacobson, Michael Milford
Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks.