Results 21 to 30 of about 1,634 (141)

RGB-D Saliency Detection via Depth Quality Perception and Hierarchical Feature Guidance [PDF]

Jisuanji gongcheng, 2023
Existing fusion-based RGB-D saliency object detection methods ignore the differences between RGB and depth map features when fusing cross-modal features.The problems from fusing unbalanced cross-modal features makes the model insufficiently leverage ...
SONG Mengke, ZHENG Yuanchao, CHEN Chenglizhao
doaj   +1 more source

RGB-D Salient Object Detection Using Saliency and Edge Reverse Attention

IEEE Access, 2023
RGB-D salient object detection is a task to detect visually significant objects in an image using RGB and depth images. Although many useful CNN-based methods have been proposed in the past, there are some problems such as blurring of object boundaries ...
Tomoki Ikeda, Masaaki Ikehara
doaj   +1 more source

Self-Supervised Pretraining for RGB-D Salient Object Detection

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Existing CNNs-Based RGB-D salient object detection (SOD) networks are all required to be pretrained on the ImageNet to learn the hierarchy features which helps provide a good initialization. However, the collection and annotation of large-scale datasets are time-consuming and expensive. In this paper, we utilize self-supervised representation learning
Xiaoqi Zhao 0003, Youwei Pang, Lihe Zhang, Huchuan Lu, Xiang Ruan   +4 more
openaire   +2 more sources

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.
Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao, Jianbing Shen, Ce Zhu   +5 more
openaire   +3 more sources

Depth‐aware lightweight network for RGB‐D salient object detection

IET Image Processing, 2023
RGB‐D salient object detection (SOD) is to detect salient objects from one RGB image and its depth data. Although related networks have achieved appreciable performance, they are not ideal for mobile devices since they are cumbersome and time‐consuming ...
Liuyi Ling, Yiwen Wang, Chengjun Wang, Shanyong Xu, Yourui Huang   +4 more
doaj   +1 more source

Coordinate Attention Filtering Depth-Feature Guide Cross-Modal Fusion RGB-Depth Salient Object Detection

Advances in Multimedia, 2023
Existing RGB + depth (RGB-D) salient object detection methods mainly focus on better integrating the cross-modal features of RGB images and depth maps.
Lingbing Meng, Mengya Yuan, Xuehan Shi, Qingqing Liu, Le Zhange, Jinhua Wu, Ping Dai, Fei Cheng   +7 more
doaj   +1 more source

LIANet: Layer Interactive Attention Network for RGB-D Salient Object Detection

IEEE Access, 2022
RGB-D salient object detection (SOD) usually describes two modes’ classification or regression problem, namely RGB and depth. The existing RGB-D SOD methods use depth hints to increase the detection performance, meanwhile they focus on the quality
Yibo Han, Liejun Wang, Anyu Du, Shaochen Jiang   +3 more
doaj   +1 more source

TANet: Transformer‐based asymmetric network for RGB‐D salient object detection

IET Computer Vision, 2023
Existing RGB‐D salient object detection methods mainly rely on a symmetric two‐stream Convolutional Neural Network (CNN)‐based network to extract RGB and depth channel features separately.
Chang Liu, Gang Yang, Shuo Wang, Hangxu Wang, Yunhua Zhang, Yutao Wang   +5 more
doaj   +1 more source

Local Background Enclosure for RGB-D Salient Object Detection [PDF]

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images. In most cases, depth contrast is used as the main feature. However, areas of high contrast in background regions cause false positives for such methods, as the background frequently contains regions that are highly variable in depth.
David Feng 0002, Nick Barnes, Shaodi You, Chris McCarthy   +3 more
openaire   +1 more source

Three‐stream RGB‐D salient object detection network based on cross‐level and cross‐modal dual‐attention fusion

IET Image Processing, 2023
The effective integration of RGB and depth map features to improve the performance of RGB‐D salient object detection (SOD) has garnered significant research interest.
Lingbing Meng, Mengya Yuan, Xuehan Shi, Qingqing Liu, Fei Cheng, Lingli Li   +5 more
doaj   +1 more source