Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection

Hao Tang¹  Zechao Li¹  Dong Zhang²  Shengfeng He³  Jinhui Tang¹

¹Nanjing University of Science and Technology, Nanjing, China
²Hong Kong University of Science and Technology, Hong Kong, China
³Singapore Management University, Singapore 

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Codes, Datasets, and Results Coming Soon!

Framework

VT-IMAG Dataset Download Link (Google Drive)

The primary purpose of the constructed VT-IMAG is to drive the advancement of RGB-T SOD methods and facilitate their deployment in real-world scenarios. For a fair comparison, all models are solely trained on clear data and simple scenes (i.e., training set of VT5000) and evaluated for Zero-shot Robustness on various real-world challenging cases in VT-IMAG.

Requirements

• Python 3.6
• Pytorch >= 1.7.0
• Torchvision = 0.10

Evaluation

We use this Saliency-Evaluation-Toolbox for evaluating all RGB-T SOD results.

Citation

Please cite our paper if you find the work useful, thanks!

@ARTICLE{10113165,
   author={Tang, Hao and Li, Zechao and Zhang, Dong and He, Shengfeng and Tang, Jinhui},
   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
   title={Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection}, 
   year={2024},
   doi={}
}

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