Results 221 to 230 of about 18,850,036 (270)
RGB-D Salient Object Detection With Ubiquitous Target Awareness [PDF]
IEEE Transactions on Image Processing, 2021 Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object
Yifan Zhao, Xiaowu Chen, Jia Li
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Bilateral Attention Network for RGB-D Salient Object Detection [PDF]
IEEE Transactions on Image Processing, 2021 Most existing RGB-D salient object detection (SOD) methods focus on the foreground region when utilizing the depth images. However, the background also provides important information in traditional SOD methods for promising performance. To better explore salient information in both foreground and background regions, this paper proposes a Bilateral ...
Wenda Jin, Zhao Zhang, Xu Jun
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Siamese Network for RGB-D Salient Object Detection and Beyond [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately designed training process.
Ge-Peng Ji, Ce Zhu, Keren Fu
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MobileSal: Extremely Efficient RGB-D Salient Object Detection [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Accepted in IEEE TPAMI, 11 pages, 11 tables, 5 ...
Ming-Ming Cheng, Yun Liu, Yu-Huan Wu
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Calibrated RGB-D Salient Object Detection
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of
Wei Ji 0011 +10 more
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Dynamic Selective Network for RGB-D Salient Object Detection
IEEE Transactions on Image Processing, 2021RGB-D saliency detection is receiving more and more attention in recent years. There are many efforts have been devoted to this area, where most of them try to integrate the multi-modal information, i.e. RGB images and depth maps, via various fusion strategies. However, some of them ignore the inherent difference between the two modalities, which leads
Zheng Bolun, Yaoqi Sun, Jiyong Zhang
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Bifurcation Fusion Network for RGB-D Salient Object Detection
Journal of Circuits, Systems and Computers, 2022With the rapid development of sensor technology, multi-modal data fusion methods based on deep neural networks provide a reliable guarantee for object recognition and detection in complex scenarios. Most of the existing RGB-D image salient object detection methods improve the salient object detection methods in 2D scenes, which have many problems, such
Zhi-Hua Zhao 0001, Li Chen
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Circular Complement Network for RGB-D Salient Object Detection
Neurocomputing, 2021Abstract With the supplement of texture and geometry cues in depth maps, some difficult scenes of salient object detection (SOD) in 2D images can be overcome. However, some distractors in the depth maps with relatively poor quality may interfere with SOD.
Zhen Bai 0001 +4 more
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Cascade Graph Neural Networks for RGB-D Salient Object Detection [PDF]
Lecture Notes in Computer Science, 2020 In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient object detection fromRGB-D images is how to fully leverage the two complementary data sources.
Zhicheng Jiao, Ao Luo
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CDNet: Complementary Depth Network for RGB-D Salient Object Detection
IEEE Transactions on Image Processing, 2021Current 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.
Ming-Ming Cheng, Wenda Jin, Qi Han
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