Cdnet: Complementary Depth Network For Rgb-D Salient Object Detection
IEEE TRANSACTIONS ON IMAGE PROCESSING(2021)
摘要
Current RGB-D salient object detection (SOD) methods utilize the depth stream as complementary information to the RGB stream. However, the depth maps are usually of low-quality in existing RGB-D SOD datasets. Most RGB-D SOD networks trained with these datasets would produce error-prone results. In this paper, we propose a novel Complementary Depth Network (CDNet) to well exploit saliency-informative depth features for RGB-D SOD. To alleviate the influence of low-quality depth maps to RGB-D SOD, we propose to select saliency-informative depth maps as the training targets and leverage RGB features to estimate meaningful depth maps. Besides, to learn robust depth features for accurate prediction, we propose a new dynamic scheme to fuse the depth features extracted from the original and estimated depth maps with adaptive weights. What's more, we design a two-stage cross-modal feature fusion scheme to well integrate the depth features with the RGB ones, further improving the performance of our CDNet on RGB-D SOD. Experiments on seven benchmark datasets demonstrate that our CDNet outperforms state-of-the-art RGB-D SOD methods. The code is publicly available at https://github.com/blanclist/CDNet.
更多查看译文
关键词
Feature extraction, Ions, Fuses, Task analysis, Object detection, Streaming media, Predictive models, RGB-D salient object detection, depth estimation, cross-modal feature fusion
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络