DMRA_RGBD-SOD

Code repository for our paper entilted "Depth-induced Multi-scale Recurrent Attention Network for Saliency Detection" accepted at ICCV 2019 (poster).

Overall

The proposed Dataset

• Dataset: DUTLF

1. Our DUTLF family consists of DUTLF-MV, DUTLF-FS, DUTLF-Depth.
2. The dataset will be expanded to 4000 about real scenes.
3. We are working on it and will make it publicly available soon.

• Dataset: DUTLF-Depth

1. The dataset is part of DUTLF dataset captured by Lytro camera, and we selected a more accurate 1200 depth map pairs for more accurate RGB-D saliency detection.
2. We create a large scale RGB-D dataset(DUTLF-Depth) with 1200 paired images containing more complex scenarios, such as multiple or transparent objects, similar foreground and background, complex background, low-intensity environment. This challenging dataset can contribute to comprehensively evaluating saliency models.

• The dataset link can be found here. And we split the dataset including 800 training set and 400 test set.

DMRA Code

> Requirment

• pytorch 0.3.0+
• torchvision
• PIL
• numpy

> Usage

1. Clone the repo

git clone https://github.com/jiwei0921/DMRA.git
cd DMRA/

2. Train/Test

• test
  Download related dataset link, and set the param '--phase' as "test" and '--param' as 'True' in demo.py. Meanwhile, you need to set dataset path and checkpoint name correctly.

python demo.py

• train
  Our train-augment dataset link [ fetch code haxl ] / train-ori dataset, and set the param '--phase' as "train" and '--param' as 'True'(loading checkpoint) or 'False'(no loading checkpoint) in demo.py. Meanwhile, you need to set dataset path and checkpoint name correctly.

python demo.py

> Training info and pre-trained models for DMRA

To better understand, we retrain our network and record some detailed training details as well as corresponding pre-trained models.

Iterations	Loss	NJUD(F-measure)	NJUD(MAE)	NLPR(F-measure)	NLPR(MAE)	download link
100W	958	0.882	0.048	0.867	0.031	link
70W	2413	0.876	0.050	0.854	0.033	link
40W	3194	0.861	0.056	0.823	0.037	link
16W	8260	0.805	0.081	0.725	0.056	link
2W	33494	0.009	0.470	0.030	0.452	link
0W	45394	-	-	-	-	-

• Tips: The results of the paper shall prevail. Because of the randomness of the training process, the results fluctuated slightly.

> Results

| DUTLF-Depth | | NJUD | | NLPR | | STEREO | | LFSD | | RGBD135 | | SSD |

• Note: For evaluation, all results are implemented on this ready-to-use toolbox.
• SIP results: This is test results on SIP dataset, and fetch code is 'fi5h'.

> Related RGB-D Saliency Datasets

All common RGB-D Saliency Datasets we collected are shared in ready-to-use manner.

• The web link is here.

If you think this work is helpful, please cite

@inproceedings{piao2019depth,
  title={Depth-induced multi-scale recurrent attention network for saliency detection},
  author={Piao, Yongri and Ji, Wei and Li, Jingjing and Zhang, Miao and Lu, Huchuan},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={7254--7263},
  year={2019}
}

Related SOTA RGB-D methods' results on our dataset

Meanwhile, we also provide other state-of-the-art RGB-D methods' results on our proposed dataset, and you can directly download their results (All results,2gs2).

No.	Pub.	Name	Title	Download
14	ICCV2019	DMRA	Depth-induced multi-scale recurrent attention network for saliency detection	results, g7rz
13	CVPR2019	CPFP	Depth-induced multi-scale recurrent attention network for saliency detection	results, g7rz
12	TIP2019	TANet	Three-stream attention-aware network for RGB-D salient object detection	results, g7rz
11	PR2019	MMCI	Multi-modal fusion network with multiscale multi-path and cross-modal interactions for RGB-D salient object detection	results, g7rz
10	ICME2019	PDNet	Pdnet: Prior-model guided depth-enhanced network for salient object detection	results, g7rz
09	CVPR2018	PCA	Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection	results, g7rz
08	ICCVW2017	CDCP	An innovative salient object detection using center-dark channel prior	results, g7rz
07	TCyb2017	CTMF	CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion	results, g7rz
06	TIP2017	DF	RGBD salient object detection via deep fusion	results, g7rz
05	CAIP2017	MB	A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining	results, g7rz
04	SPL2016	DCMC	Saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion	results, g7rz
03	ECCV2014	LHM-NLPR	Rgbd salient object detection: a benchmark and algorithms	results, g7rz
02	ICIP2014	ACSD	Depth saliency based on anisotropic center-surround difference	results, g7rz
01	ICIMCS2014	DES	Depth enhanced saliency detection method	results, g7rz

• Thanks for related authors to provide the code or results, particularly, Deng-ping Fan, Hao Chen, Chun-biao Zhu, etc.

Contact Us

If you have any questions, please contact us ( wji3@ualberta.ca or weiji.dlut@gmail.com ).