10 citations · 21 across the 5 of their papers we have counts for
5 papers
Semantic Segmentation from Limited Training Data
A. Milan, T. Pham, K. Vijay +22
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transpa…
Episode-Based Active Learning with Bayesian Neural Networks
Feras Dayoub, Niko Sünderhauf, Peter Corke
We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, s…
Place Categorization and Semantic Mapping on a Mobile Robot
Niko Sünderhauf, Feras Dayoub, Sean McMahon +6
In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success…
Subset Feature Learning for Fine-Grained Category Classification
Zongyuan Ge, Christopher Mccool, Conrad Sanderson +1
Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning…
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
ZongYuan Ge, Chris McCool, Conrad Sanderson +1
We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have co…