The performance of recent RGB-D salient object detectors has significantly improved owing to their integration of convolutional neural networks (CNNs). However, most existing salient object detection (SOD) methods represent features using a VGGNet backbone, which lacks the ability to retain complete RGB and depth modals and must compensate by applying several skip connections. In this letter, we propose a gate fusion network (GFNet) with Res2Net architecture to solve this problem. GFNet consists of two interacting Res2Net block encoder streams and four gate fusion block (GFB) decoders to interconnect the streams and fuse features. Res2Net blocks have a robust feature retention mechanism to ensure that the decoders can learn complete information, while the GFB formulates the interdependences of the encoders and eliminates noise via a gate mechanism. We evaluated GFNet using two popular RGB-D salient detection benchmark datasets (NJU2000 and NLPR) and achieved state-of-the art performance.

中文翻译：

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GFNet：带有 Res2Net 的门融合网络，用于检测 RGB-D 图像中的显着对象

由于集成了卷积神经网络 (CNN)，最近的 RGB-D 显着对象检测器的性能得到了显着提高。然而，大多数现有的显着对象检测 (SOD) 方法使用 VGGNet 主干表示特征，该主干缺乏保留完整 RGB 和深度模态的能力，必须通过应用多个跳过连接来进行补偿。在这封信中，我们提出了一个具有 Res2Net 架构的门融合网络（GFNet）来解决这个问题。GFNet 由两个相互作用的 Res2Net 块编码器流和四个门融合块 (GFB) 解码器组成，用于互连流和融合特征。Res2Net 块具有强大的特征保留机制，以确保解码器可以学习完整的信息，而 GFB 则制定了编码器的相互依赖性并通过门机制消除噪声。