MXNet version of the code for our NeurIPS'19 paper "Learning Deep Bilinear Transformation for Fine-grained Image Representation"
Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations. The proposed DBTNet can deeply integrate bilinear features into CNN to learn fine-grained image representations.
| Method | Dimension | CUB-200-2011 | Stanford-Car | Aircraft |
|---|---|---|---|---|
| Compact Bilinear | 14k | 81.6 | 88.6 | 81.6 |
| Kernel Pooling | 14k | 84.7 | 91.1 | 85.7 |
| iSQRT-COV | 8k | 87.3 | 91.7 | 89.5 |
| iSQRT-COV | 32k | 88.1 | 92.8 | 90.0 |
| DBTNet-50 (ours) | 2k | 87.5 | 94.1 | 91.2 |
| DBTNet-101 (ours) | 2k | 88.1 | 94.5 | 91.6 |
MXNet 1.3.1
GluonCV 0.3.0
download the imagenet data:
cd data/imagenet/
wget https://australiav100data.blob.core.windows.net/heliang/imagenet_train.rec
wget https://australiav100data.blob.core.windows.net/heliang/imagenet_train.idx
wget https://australiav100data.blob.core.windows.net/heliang/imagenet_val.rec
wget https://australiav100data.blob.core.windows.net/heliang/imagenet_val.idx
download the CUB-200-2011 dataset:
cd data/
wget https://australiav100data.blob.core.windows.net/heliang/cub.tar
tar -xvf cub.tar
cd code/
bash train_imagenet_dbt.sh
The ImageNet pretrained model is available.
cd code/
bash ft_cub_dbt.sh
On going. Welcome to reimplement and share the DBT code in pytorch.
If any part of our paper and code is helpful to your work, please generously cite with:
@incollection{NIPS2019_8680,
title = {Learning Deep Bilinear Transformation for Fine-grained Image Representation},
author = {Zheng, Heliang and Fu, Jianlong and Zha, Zheng-Jun and Luo, Jiebo},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {4279--4288},
year = {2019}