odisal

This repository contains the pytorch implementation of omni-directional saliency map estimation in IEICE journal paper:

• Takao Yamanaka, Tatsuya Suzuki, Taiki Nobutsune, and Chenjunlin Wu, "Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias," IEICE Transactions on Information and Systems, Vol. E106-D, No. 10, 2023. https://arxiv.org/abs/2309.08139

Downloads

In addition to this repository, download the following zip files, unzip them, and place them under the odisal folders.

• DataSets.zip (5.94GB): odisal/Datasets
• pretrained.zip (928.3MB): odisal/pretrained
• maps.zip (36.2MB): odisal/program/maps
• pytorch_dpn_pretrained.zip (1.1GB): odisal/program/models/pytorch_dpn_pretrained
• deepgaze_pytorch.zip (389.3MB): odisal/program/models/deepgaze_pytorch

Since the following folders include only output files, it does not have to be downloaded.

• logs.zip (2.53GB): odisal/program/logs
• eval.zip (53KB): odisal/program/eval
• outputs.zip (72MB): odisal/program/outputs

Examples

Multi-Scale Estimation (Multi-Angles of View; MAV)

> cd program
> python pipeline.py --cfg config/DenseSal_ch512_100_110_120.yaml

Signle-Scale Estimation (Single Angle of View; SAV)

> cd program
> python pipeline.py --cfg config/DenseSal_BA2_with_features_ch512_100.yaml

'pipeline.py' excecutes fine-tuning, estimation, and evaluation for an omni-directional image dataset with the settings in 'program/confing/*.yaml'.

---

A trained model for MAV with DenseSal is avalabile in
'program/logs/salient360_2018_BA2_with_feature_100_110_120/densesalbi3/ckpt/checkpoint_20230903-195522_model_best.pth.tar'

If you want to estimate saliency maps for omni-directional images (ODI) with the trained model without training,

1. Comment out 'Train:' and 'EVALUATE:' parts in 'program/config/DenseSal_ch512_100_110_120.yaml'. Only leave the 'ESTIMATE:' part.
2. Set the path to the model file in 'Resume' in the yaml file.

ESTIMATE > SETTING > RESUME: logs/salient360_2018_BA2_with_feature_100_110_120/densesalbi3/ckpt/checkpoint_20230903-195522_model_best.pth.tar

3. Run the pipeline.py.

python pipeline.py --cfg config/DenseSal_ch512_100_110_120.yaml

The estimated ODI saliency maps will be obtained in the 'outputs' folder.

Settings in config (program/config)

Settings for MainNet (densesalbi3 / dpnsal131_dilation_multipath / deepgaze2e)

ARCH: densesalbi3

---

Settings for Multi-scale estimation

MAV (Multi-Angles of View)

VIEW_ANGLE: [100, 110, 120 ]
USE_MULTISCALE_MODEL: True

DATASET_CFG_PATH: ../DataSets/salient360_2018_BA2_with_feature_100_110_120/salient360_2018_BA2_with_features_E26_1600_800_500_500_100_110_120.cfg

SAV (Single Angle of View)

VIEW_ANGLE: 100
USE_MULTISCALE_MODEL: False

DATASET_CFG_PATH: ../DataSets/salient360_2018_SVA_100/salient360_2018_SVA_E26_1600_800_500_500.cfg

---

Settings for the integration layer (Table 1)

Arch. 1

USE_FUSION_LAYER: False
USE_ATTENTION_LAYER: True
USE_BASIC_ATTENTION_LAYER: True
USE_BASIC_ATTENTION_V2_LAYER: False
USE_BASIC_ATTENTION_WITH_FEATURE: False

Arch. 2

USE_FUSION_LAYER: False
USE_ATTENTION_LAYER: True
USE_BASIC_ATTENTION_LAYER: False
USE_BASIC_ATTENTION_V2_LAYER: True
USE_BASIC_ATTENTION_WITH_FEATURE: False

Arch. 3

USE_FUSION_LAYER: False
USE_ATTENTION_LAYER: True
USE_BASIC_ATTENTION_LAYER: True
USE_BASIC_ATTENTION_V2_LAYER: False
USE_BASIC_ATTENTION_WITH_FEATURE: True

Arch. 4

USE_FUSION_LAYER: False
USE_ATTENTION_LAYER: True
USE_BASIC_ATTENTION_LAYER: False
USE_BASIC_ATTENTION_V2_LAYER: True
USE_BASIC_ATTENTION_WITH_FEATURE: True

References

1. Takao Yamanaka, Tatsuya Suzuki, Taiki Nobutsune, and Chenjunlin Wu, "Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias," IEICE Transactions on Information and Systems, Vol. E106-D, No. 10, 2023. https://arxiv.org/abs/2309.08139
2. T. Suzuki and T. Yamanaka, "Saliency Map Estimation for Omni-Directional Image Considering Prior Distributions," IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, Miyazaki, Japan. http://arxiv.org/abs/1807.06329
3. T. Oyama and T. Yamanaka, "Influence of Image Classification Accuracy on Saliency Map Estimation," CAAI Transactions on Intelligence Technology, vol. 3, issue 3, 2018, pp. 140-152. https://digital-library.theiet.org/content/journals/10.1049/trit.2018.1012 https://arxiv.org/abs/1807.10657
4. Akis Linardos, Matthias Kümmerer, Ori Press, and Matthias Bethge, "DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling," ICCV 2021. https://arxiv.org/abs/2105.12441 https://github.com/matthias-k/DeepGaze

Versions

The codes were confirmed with the following versions.

• Python 3.9.7
• PyTorch 2.0.0+cu117
• NVIDIA Driver: 510.47.03
• CUDA: 11.6