|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM358490693 |
| 003 |
DE-627 |
| 005 |
20231226074843.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2023.3287506
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1194.xml
|
| 035 |
|
|
|a (DE-627)NLM358490693
|
| 035 |
|
|
|a (NLM)37347635
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhang, Yuhang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Learning Shape-Invariant Representation for Generalizable Semantic Segmentation
|
| 264 |
|
1 |
|c 2023
|
| 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 11.09.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Semantic segmentation assigns a category for each pixel and has achieved great success in a supervised manner. However, it fails to generalize well in new domains due to the domain gap. Domain adaptation is a popular way to solve this issue, but it needs target data and cannot handle unavailable domains. In domain generalization (DG), the model is trained without the target data and DG aims to generalize well in new unavailable domains. Recent works reveal that shape recognition is beneficial for generalization but still lack exploration in semantic segmentation. Meanwhile, the object shapes also exist a discrepancy in different domains, which is often ignored by the existing works. Thus, we propose a Shape-Invariant Learning (SIL) framework to focus on learning shape-invariant representation for better generalization. Specifically, we first define the structural edge, which considers both the object boundary and the inner structure of the object to provide more discrimination cues. Then, a shape perception learning strategy including a texture feature discrepancy reduction loss and a structural feature discrepancy enlargement loss is proposed to enhance the shape perception ability of the model by embedding the structural edge as a shape prior. Finally, we use shape deformation augmentation to generate samples with the same content and different shapes. Essentially, our SIL framework performs implicit shape distribution alignment at the domain-level to learn shape-invariant representation. Extensive experiments show that our SIL framework achieves state-of-the-art performance
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Tian, Shishun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liao, Muxin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Hua, Guoguang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zou, Wenbin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Xu, Chen
|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 32(2023) vom: 01., Seite 5031-5045
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:32
|g year:2023
|g day:01
|g pages:5031-5045
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2023.3287506
|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 32
|j 2023
|b 01
|h 5031-5045
|