|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM322046149 |
| 003 |
DE-627 |
| 005 |
20231225181210.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2021 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2021.3060167
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1073.xml
|
| 035 |
|
|
|a (DE-627)NLM322046149
|
| 035 |
|
|
|a (NLM)33646949
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Jin, Wen-Da
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a CDNet
|b Complementary Depth Network for RGB-D Salient Object Detection
|
| 264 |
|
1 |
|c 2021
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 10.03.2021
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a 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
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Xu, Jun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Han, Qi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Yi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Cheng, Ming-Ming
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 26., Seite 3376-3390
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:30
|g year:2021
|g day:26
|g pages:3376-3390
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2021.3060167
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 30
|j 2021
|b 26
|h 3376-3390
|