TRACER: Extreme Attention Guided Salient Object Tracing Network

This paper was accepted at AAAI 2022 SA poster session. [pdf]

Datasets

All datasets are available in public.

Download the DUTS-TR and DUTS-TE from Here
Download the DUT-OMRON from Here
Download the HKU-IS from Here
Download the ECSSD from Here
Download the PASCAL-S from Here
Download the edge GT from Here.

Data structure


TRACER
├── data
│   ├── DUTS
│   │   ├── Train
│   │   │   ├── images
│   │   │   ├── masks
│   │   │   ├── edges
│   │   ├── Test
│   │   │   ├── images
│   │   │   ├── masks
│   ├── DUT-O
│   │   ├── Test
│   │   │   ├── images
│   │   │   ├── masks
│   ├── HKU-IS
│   │   ├── Test
│   │   │   ├── images
│   │   │   ├── masks
      .
      .
      .

Requirements

Python >= 3.7.x
Pytorch >= 1.8.0
albumentations >= 0.5.1
tqdm >=4.54.0
scikit-learn >= 0.23.2

Run

Run main.py scripts.


# For training TRACER-TE0 (e.g.)
python main.py train --arch 0 --img_size 320

# For testing TRACER with pre-trained model (e.g.)  
python main.py test --exp_num 0 --arch 0 --img_size 320

Pre-trained models of TRACER are available at here
Change the model name as 'best_model.pth' and put the weights to the path 'results/DUTS/TEx_0/best_model.pth'
(here, the x means the model scale e.g., 0 to 7).
Input image sizes for each model are listed belows.

Configurations

--arch: EfficientNet backbone scale: TE0 to TE7.
--frequency_radius: High-pass filter radius in the MEAM.
--gamma: channel confidence ratio \gamma in the UAM.
--denoise: Denoising ratio d in the OAM.
--RFB_aggregated_channel: # of channels in receptive field blocks.
--multi_gpu: Multi-GPU learning options.
--img_size: Input image resolution.

Model	Img size
TRACER-Efficient-0 ~ 1	320
TRACER-Efficient-2	352
TRACER-Efficient-3	384
TRACER-Efficient-4	448
TRACER-Efficient-5	512
TRACER-Efficient-6	576
TRACER-Efficient-7	640

Citation


@article{lee2021tracer,
  title={TRACER: Extreme Attention Guided Salient Object Tracing Network},
  author={Lee, Min Seok and Shin, WooSeok and Han, Sung Won},
  journal={arXiv preprint arXiv:2112.07380},
  year={2021}
}