ILLUME

To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors.

Winter Conference on Applications of Computer Vision (WACV) 2022.

Paper / Talk / Poster / Supplementary

Adaptive object detection remains challenging due to visual diversity in background scenes and intricate combinations of objects. Motivated by structural importance, we aim to attend prominent instance-specific regions, overcoming the feature misalignment issue.

We propose a novel resIduaL seLf-attentive featUre alignMEnt ( ILLUME ) method for adaptive object detection. ILLUME comprises Self-Attention Feature Map (SAFM) module that enhances structural attention to object-related regions and thereby generates domain invariant features.

Our approach significantly reduces the domain distance with the improved feature alignment of the instances.

Setup Introduction

Follow faster-rcnn repository to setup the environment. When installing pytorch-faster-rcnn, you may encounter some issues. Many issues have been reported there to setup the environment. We used Pytorch 0.4.1 for this project. The different version of pytorch will cause some errors, which have to be handled based on each envirionment.

Tested Hardwards & Softwares

• GTX 1080
• Pytorch 0.4.1
• CUDA 9.2

conda install pytorch=0.4.1 torchvision==0.2.1 cuda92 -c pytorch

• Before training:

mkdir data
cd lib
sh make.sh (add -gencode arch=compute_70,code=sm_70" # added for GTX10XX)

• Notes before running : This training code is setup to be ran on 16 GB GPU. You may have to make some adjustments if you do not have this hardware available.
• Tensorboard tensorboard --logdir='your/path/here'

Data Preparation

• PASCAL_VOC 07+12: Please follow the instructions in py-faster-rcnn to prepare VOC datasets.
• Clipart, WaterColor: Dataset preparation instruction link Cross Domain Detection .
• Sim10k: Website Sim10k
• CitysScape, FoggyCityscape: Download website Cityscape, see dataset preparation code in DA-Faster RCNN

All codes are written to fit for the format of PASCAL_VOC. For example, the dataset Sim10k is stored as follows.

$ cd Sim10k/VOC2012/
$ ls
Annotations  ImageSets  JPEGImages
$ cat ImageSets/Main/val.txt
3384827.jpg
3384828.jpg
3384829.jpg
.
.
.

If you want to test the code on your own dataset, arange the dataset in the format of PASCAL, make dataset class in lib/datasets/. and add it to lib/datasets/factory.py, lib/datasets/config_dataset.py. Then, add the dataset option to lib/model/utils/parser_func.py.

Data Path

Write your dataset directories' paths in lib/datasets/config_dataset.py.

Pretrained Model

We used two models pre-trained on ImageNet in our experiments, VGG and ResNet101. You can download these two models from:

• VGG16: Dropbox

• ResNet101: Dropbox

Download them and write the path in __C.VGG_PATH and __C.RESNET_PATH at lib/model/utils/config.py.

Train

• Cityscapes --> Foggy_cityscapes

python trainval_net_ILLUME.py --cuda --net vgg16 --dataset cityscape --dataset_t foggy_cityscape

use tensorboard

python trainval_net_ILLUME.py --cuda --net vgg16 --dataset cityscape --dataset_t foggy_cityscape --use_tfb

--use_tfb will enable tensorboard to record training results

Test

• Cityscapes --> Foggy_cityscapes

python test_net_ILLUME.py --cuda --net vgg16 --dataset foggy_cityscape --load_name models/vgg16/cityscape/*.pth

Citation

@inproceedings{khindkar2022miss,
  title={To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors},
  author={Khindkar, Vaishnavi and Arora, Chetan and Balasubramanian, Vineeth N and Subramanian, Anbumani and Saluja, Rohit and Jawahar, CV},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={3632--3642},
  year={2022}
}