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
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Requirements
- Python 3.7.x
- Pytorch >= 1.8.0
- albumentations >= 0.5.1
- matplotlib >= 3.3.3
- 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
Configurations
–img_size: Input image resolution.
–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.
Citation
@misc{lee2021tracer,
title={TRACER: Extreme Attention Guided Salient Object Tracing Network},
author={Min Seok Lee and WooSeok Shin and Sung Won Han},
year={2021},
eprint={2112.07380},
archivePrefix={arXiv},
primaryClass={cs.CV}
}