论文标题
RGB-D显着对象检测的逐步引导的替代精炼网络
Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection
论文作者
论文摘要
在本文中，我们旨在为RGB-D显着对象检测开发一个高效而紧凑的深层网络，其中深度图像提供了互补的信息以在复杂的场景中提高性能。从多尺度残差块的粗糙初始预测开始，我们提出了一个逐步引导的替代精炼网络来完善它。我们首先通过从头开始学习Imagenet预训练的骨干网络，而是首先构建一个轻量级的深度流，该流可以更有效地提取互补特征，而冗余性较小。然后，与现有的基于融合的方法不同，RGB和深度特征交替地馈入拟议的指导残留（GR）块，以减少它们的相互降解。通过在每个边输出内的堆叠GR块中分配渐进式指导，可以很好地修复错误的检测和丢失的零件。在七个基准数据集上进行的广泛实验表明，我们的模型的表现优于现有的最新方法，并且还显示出效率（71 fps）和模型大小（64.9 MB）的优势。
In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).
