no code implementations • 15 Mar 2016 • Qichang Hu, Peng Wang, Chunhua Shen, Anton Van Den Hengel, Fatih Porikli
In this work, we show that by re-using the convolutional feature maps (CFMs) of a deep convolutional neural network (DCNN) model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms.
no code implementations • 12 Oct 2015 • Qichang Hu, Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel, Fatih Porikli
The proposed framework consists of a dense feature extractor and detectors of three important classes.
1 code implementation • 4 Apr 2017 • Qichang Hu, Huibing Wang, Teng Li, Chunhua Shen
By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature.
Ranked #1 on Fine-Grained Image Classification on CarFlag-563