|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM268253897 |
| 003 |
DE-627 |
| 005 |
20231224222319.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2017 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2016.2636150
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0894.xml
|
| 035 |
|
|
|a (DE-627)NLM268253897
|
| 035 |
|
|
|a (NLM)28114002
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Yunchao Wei
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a STC
|b A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation
|
| 264 |
|
1 |
|c 2017
|
| 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 Completed 11.12.2018
|
| 500 |
|
|
|a Date Revised 11.12.2018
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. In this paper, we propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically, we first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i.e., those with a single category of major object(s) and clean background). These saliency maps can be automatically obtained by existing bottom-up salient object detection techniques, where no supervision information is needed. Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations. Finally, more pixel-level segmentation masks of complex images (two or more categories of objects with cluttered background), which are inferred by using Enhanced-DCNN and image-level annotations, are utilized as the supervision information to learn the Powerful-DCNN for semantic segmentation. Our method utilizes 40K simple images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely boosting the segmentation network. Extensive experimental results on PASCAL VOC 2012 segmentation benchmark well demonstrate the superiority of the proposed STC framework compared with other state-of-the-arts
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Xiaodan Liang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yunpeng Chen
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Xiaohui Shen
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Ming-Ming Cheng
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Jiashi Feng
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yao Zhao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Shuicheng Yan
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 11 vom: 15. Nov., Seite 2314-2320
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:39
|g year:2017
|g number:11
|g day:15
|g month:11
|g pages:2314-2320
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2016.2636150
|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 39
|j 2017
|e 11
|b 15
|c 11
|h 2314-2320
|