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Multi-interactive Encoder-decoder Network for RGBT Salient Object Detection

RGBT salient object detection (SOD) aims to segment the common prominent regions of visible and thermal infrared images. Existing RGBT SOD methods don't fully explore and exploit the potentials of complementarity of different modalities and the global context of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive Siamese decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode RGB and thermal image pair into multi-level multi-modal representation. Then, we design a novel Siamese decoder to integrate the multi-level interactions of dual modalities and global contexts. With these interactions, our method works well in diversely challenging scenarios even in the presence of invalid modality. Moreover, the Siamese decoder employs label supervision to drive feature learning in each modality and the modality prejudice is thus suppressed. Finally, we carry out extensive experiments on several benchmark datasets, and the results show that the proposed method achieves the outstanding performance against state-of-the-art algorithms. The source code has released at

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