316 citations · 606 across the 8 of their papers we have counts for
9 papers
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
Xiaofeng Liu, Zhenhua Guo, Site Li +4
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the c…
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning
Thanh-Toan Do, Toan Tran, Ian Reid +3
We propose a method that substantially improves the efficiency of deep distance metric learning based on the optimization of the triplet loss function. One epoch of such training p…
Multi-modal Cycle-consistent Generalized Zero-Shot Learning
Rafael Felix, B. G. Vijay Kumar, Ian Reid +1
In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes a…
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…
Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge
D. Morrison, A. W. Tow, M. McTaggart +19
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. Th…
Smart Mining for Deep Metric Learning
Ben Harwood, Vijay Kumar B G, Gustavo Carneiro +2
To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples f…