DBTNet

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.

Framework

Main Results

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

Prerequisites

MXNet 1.3.1

GluonCV 0.3.0

Quick Start

Prepare the data:

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

Train the model on ImageNet dataset:

cd code/
bash train_imagenet_dbt.sh

Fine-tune the model on CUB-200-2011 dataset:

The ImageNet pretrained model is available.

cd code/
bash ft_cub_dbt.sh

Pytorch Version

On going. Welcome to reimplement and share the DBT code in pytorch.

Citation

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}