TRACER: Extreme Attention Guided Salient Object Tracing Network

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

PWC
PWC
PWC
PWC
PWC

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}
}

GitHub

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