323 citations · 2k across the 63 of their papers we have counts for
28 papers · 1 filter
Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
ZongYuan Ge, Chris McCool, Conrad Sanderson +4
Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to i…
Towards Miss Universe Automatic Prediction: The Evening Gown Competition
Johanna Carvajal, Arnold Wiliem, Conrad Sanderson +1
Can we predict the winner of Miss Universe after watching how they stride down the catwalk during the evening gown competition? Fashion gurus say they can! In our work, we study th…
Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors
Johanna Carvajal, Chris McCool, Brian Lovell +1
We propose a hierarchical approach to multi-action recognition that performs joint classification and segmentation. A given video (containing several consecutive actions) is proces…
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
Johanna Carvajal, Arnold Wiliem, Chris McCool +2
We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian mani…
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
ZongYuan Ge, Alex Bewley, Christopher McCool +3
We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification…
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…