论文标题
用于RGB-thermal显着对象检测的多相互作用的双动物
Multi-interactive Dual-decoder for RGB-thermal Salient Object Detection
论文作者
论文摘要
RGB热对象检测（SOD）旨在分割可见图像的共同突出区域和我们称为RGBT SOD的相应热红外图像。现有方法并未充分探索和利用不同模态和多类图像内容的互补性的潜力，这在实现准确的结果中起着至关重要的作用。在本文中，我们提出了一个多相互作用的双重模型来挖掘并建模多类型相互作用，以进行准确的RGBT SOD。在具体上，我们首先将两种模式编码为多级多模式特征表示。然后，我们设计了一种新颖的双重编码器，以进行多层次特征，两种模式和全球环境的相互作用。通过这些相互作用，即使在存在无效的方式的情况下，我们的方法在各种挑战的场景中都可以很好地发挥作用。最后，我们对公共RGBT和RGBD SOD数据集进行了广泛的实验，结果表明，所提出的方法实现了针对最先进的算法的出色性能。源代码已在以下网址发布：https：//github.com/lz118/multi-interactive-dual-decoder。
RGB-thermal salient object detection (SOD) aims to segment the common prominent regions of visible image and corresponding thermal infrared image that we call it RGBT SOD. Existing methods don't fully explore and exploit the potentials of complementarity of different modalities and multi-type cues of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive dual-decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode two modalities into multi-level multi-modal feature representations. Then, we design a novel dual-decoder to conduct the interactions of multi-level features, two modalities and global contexts. With these interactions, our method works well in diversely challenging scenarios even in the presence of invalid modality. Finally, we carry out extensive experiments on public RGBT and RGBD SOD datasets, and the results show that the proposed method achieves the outstanding performance against state-of-the-art algorithms. The source code has been released at:https://github.com/lz118/Multi-interactive-Dual-decoder.
