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Multiscale Multilevel Context and Multimodal Fusion for RGB-D Salient Object Detection
Signal Processing ( IF 4.4 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.sigpro.2020.107766
Junwei Wu , Wujie Zhou , Ting Luo , Lu Yu , Jingsheng Lei

Abstract Red–green–blue and depth (RGB-D) saliency detection has recently attracted much research attention; however, the effective use of depth information remains challenging. This paper proposes a method that leverages depth information in clear shapes to detect the boundary of salient objects. As context plays an important role in saliency detection, the method incorporates a proposed end-to-end multiscale multilevel context and multimodal fusion network (MCMFNet) to aggregate multiscale multilevel context feature maps for accurate saliency detection from objects of varying sizes. Finally, a coarse-to-fine approach is applied to an attention module retrieving multilevel and multimodal feature maps to produce the final saliency map. A comprehensive loss function is also incorporated in MCMFNet to optimize the network parameters. Extensive experiments demonstrate the effectiveness of the proposed method and its substantial improvement over state-of-the-art methods for RGB-D salient object detection on four representative datasets.

中文翻译:

用于 RGB-D 显着目标检测的多尺度多级上下文和多模态融合

摘要 红-绿-蓝和深度(RGB-D)显着性检测最近引起了很多研究的关注。然而,深度信​​息的有效利用仍然具有挑战性。本文提出了一种利用清晰形状中的深度信息来检测显着对象边界的方法。由于上下文在显着性检测中起着重要作用,该方法结合了提出的端到端多尺度多级上下文和多模态融合网络 (MCMFNet) 来聚合多尺度多级上下文特征图,以便从不同大小的对象中进行准确的显着性检测。最后,将粗到细的方法应用于检索多级和多模态特征图的注意力模块,以生成最终的显着图。MCMFNet 中还包含一个全面的损失函数来优化网络参数。
更新日期:2021-01-01
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