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Multi-interactive-Dual-decoder-for-RGBT-Salient-Object-Detection

The pytorch implementation of Multi-interactive Dual-decoder for RGBT Salient Object Detection

Train

• We use VT5000-Train to train our network. All the datasets are available in https://github.com/lz118/RGBT-Salient-Object-Detection
• The pretrained model (VGG16) can be downloaded at https://pan.baidu.com/s/11lq3mUGRFP7TFvH9Eui14A [3513]

Test

• The trained models on RGB-T Dataset

  https://pan.baidu.com/s/1Wj6bfi7lhp1KF5iCSVj0gQ [4zkx]

  https://drive.google.com/file/d/11lU5TaRZMTXQ6QCbBLinG9iDvIUDrRP5/view?usp=sharing

• The trained models on RGB-D Dataset

  https://pan.baidu.com/s/1KlAKrVszQisG0bK1kiedzA [2ulc]

  https://drive.google.com/file/d/1LKVn3iPDBI07DUBiirm4bk2-7yA3pTSM/view?usp=sharing

Evalution

• For RGB-T SOD, we provide the our saliency maps on VT821, VT1000 and VT5000-Test.

  https://pan.baidu.com/s/1hEZJyEJ2j1n1JKUgUaZZLQ [0div]

• The saliency maps of all compared methods on VT821, VT1000 and VT5000-Test. https://pan.baidu.com/s/1s_pJ5qNJcQ8Q7ucusZHnRg [ax96]

• For RGB-D SOD, we provide the our saliency maps on SIP, SSD，STERE，LFSD and DES.

  https://pan.baidu.com/s/1ZHxvMh818RxlZGW1hQA70w [2oqx]

• The evalution toolbox is provided by https://github.com/jiwei0921/Saliency-Evaluation-Toolbox