|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM355275309 |
| 003 |
DE-627 |
| 005 |
20231226064015.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2023.3243048
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1184.xml
|
| 035 |
|
|
|a (DE-627)NLM355275309
|
| 035 |
|
|
|a (NLM)37022836
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Peng, Zhiliang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Conformer
|b Local Features Coupling Global Representations for Recognition and Detection
|
| 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 Completed 03.07.2023
|
| 500 |
|
|
|a Date Revised 03.07.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a With convolution operations, Convolutional Neural Networks (CNNs) are good at extracting local features but experience difficulty to capture global representations. With cascaded self-attention modules, vision transformers can capture long-distance feature dependencies but unfortunately deteriorate local feature details. In this paper, we propose a hybrid network structure, termed Conformer, to take both advantages of convolution operations and self-attention mechanisms for enhanced representation learning. Conformer roots in feature coupling of CNN local features and transformer global representations under different resolutions in an interactive fashion. Conformer adopts a dual structure so that local details and global dependencies are retained to the maximum extent. We also propose a Conformer-based detector (ConformerDet), which learns to predict and refine object proposals, by performing region-level feature coupling in an augmented cross-attention fashion. Experiments on ImageNet and MS COCO datasets validate Conformer's superiority for visual recognition and object detection, demonstrating its potential to be a general backbone network
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Guo, Zonghao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Huang, Wei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Yaowei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Xie, Lingxi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Jiao, Jianbin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tian, Qi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Ye, Qixiang
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 8 vom: 07. Aug., Seite 9454-9468
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:8
|g day:07
|g month:08
|g pages:9454-9468
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2023.3243048
|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 45
|j 2023
|e 8
|b 07
|c 08
|h 9454-9468
|