Yuan, W.[Wei],
Xu, W.B.[Wen-Bo],
MSST-Net: A Multi-Scale Adaptive Network for Building Extraction from
Remote Sensing Images Based on Swin Transformer,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Xu, X.K.[Xiang-Kai],
Feng, Z.J.[Zhe-Jun],
Cao, C.Q.[Chang-Qing],
Li, M.Y.[Meng-Yuan],
Wu, J.[Jin],
Wu, Z.Y.[Zeng-Yan],
Shang, Y.J.[Ya-Jie],
Ye, S.B.[Shu-Bing],
An Improved Swin Transformer-Based Model for Remote Sensing Object
Detection and Instance Segmentation,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Hao, S.Y.[Si-Yuan],
Wu, B.[Bin],
Zhao, K.[Kun],
Ye, Y.X.[Yuan-Xin],
Wang, W.[Wei],
Two-Stream Swin Transformer with Differentiable Sobel Operator for
Remote Sensing Image Classification,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link
2204
BibRef
Gong, H.[Hang],
Mu, T.K.[Ting-Kui],
Li, Q.X.[Qiu-Xia],
Dai, H.S.[Hai-Shan],
Li, C.L.[Chun-Lai],
He, Z.P.[Zhi-Ping],
Wang, W.J.[Wen-Jing],
Han, F.[Feng],
Tuniyazi, A.[Abudusalamu],
Li, H.Y.[Hao-Yang],
Lang, X.C.[Xue-Chan],
Li, Z.Y.[Zhi-Yuan],
Wang, B.[Bin],
Swin-Transformer-Enabled YOLOv5 with Attention Mechanism for Small
Object Detection on Satellite Images,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Xiao, X.[Xiao],
Guo, W.L.[Wen-Liang],
Chen, R.[Rui],
Hui, Y.L.[Yi-Long],
Wang, J.N.[Jia-Ning],
Zhao, H.Y.[Hong-Yu],
A Swin Transformer-Based Encoding Booster Integrated in U-Shaped
Network for Building Extraction,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Yang, S.[Sihan],
Song, F.[Fei],
Jeon, G.G.[Gwang-Gil],
Sun, R.[Rui],
Scene Changes Understanding Framework Based on Graph Convolutional
Networks and Swin Transformer Blocks for Monitoring LCLU Using
High-Resolution Remote Sensing Images,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Yao, J.Y.[Jun-Yuan],
Jin, S.G.[Shuang-Gen],
Multi-Category Segmentation of Sentinel-2 Images Based on the Swin
UNet Method,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Sun, F.[Fan],
Zhou, W.[Wujie],
Ye, L.[Lv],
Yu, L.[Lu],
Hierarchical Decoding Network Based on Swin Transformer for Detecting
Salient Objects in RGB-T Images,
SPLetters(29), 2022, pp. 1714-1718.
IEEE DOI
2208
Feature extraction, Semantics, Decoding, Transformers, Convolution,
Training, Image segmentation, Transformer, hierarchical decoder,
global saliency per- ception
BibRef
Zhou, K.[Kexue],
Zhang, M.[Min],
Wang, H.[Hai],
Tan, J.L.[Jin-Lin],
Ship Detection in SAR Images Based on Multi-Scale Feature Extraction
and Adaptive Feature Fusion,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Li, K.Y.[Kuo-Yang],
Zhang, M.[Min],
Xu, M.P.[Mai-Ping],
Tang, R.[Rui],
Wang, L.[Liang],
Wang, H.[Hai],
Ship Detection in SAR Images Based on Feature Enhancement SWIN
Transformer and Adjacent Feature Fusion,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Zheng, F.J.[Fu-Jian],
Lin, S.[Shuai],
Zhou, W.[Wei],
Huang, H.[Hong],
A Lightweight Dual-Branch Swin Transfomrer for Remote Sensing Scene
Classification,
RS(15), No. 11, 2023, pp. 2865.
DOI Link
2306
BibRef
Peng, Y.B.[Yin-Bin],
Ren, J.S.[Jian-Si],
Wang, J.M.[Jia-Mei],
Shi, M.L.[Mei-Lin],
Spectral-Swin Transformer with Spatial Feature Extraction Enhancement
for Hyperspectral Image Classification,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link
2306
BibRef
Yuan, W.[Wei],
Zhang, X.B.[Xiao-Bo],
Shi, J.[Jibao],
Wang, J.[Jin],
LiteST-Net: A Hybrid Model of Lite Swin Transformer and Convolution
for Building Extraction from Remote Sensing Image,
RS(15), No. 8, 2023, pp. 1996.
DOI Link
2305
BibRef
Liu, B.S.[Bai-Sen],
Liu, Y.J.[Yuan-Jia],
Zhang, W.[Wulin],
Tian, Y.[Yiran],
Kong, W.[Weili],
Spectral Swin Transformer Network for Hyperspectral Image
Classification,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Zhong, B.[Bo],
Wei, T.F.[Teng-Fei],
Luo, X.B.[Xiao-Bo],
Du, B.[Bailin],
Hu, L.F.[Long-Fei],
Ao, K.[Kai],
Yang, A.[Aixia],
Wu, J.J.[Jun-Jun],
Multi-Swin Mask Transformer for Instance Segmentation of Agricultural
Field Extraction,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
BibRef
Ren, Y.G.[Yong-Gong],
Xu, W.Q.[Wen-Qiang],
Mao, Y.Y.X.[Yuan-Yan-Xin],
Wu, Y.[Yuechu],
Fu, B.[Bo],
Thanh, D.N.H.[Dang N. H.],
Few-shot learning for dermatological conditions with Lesion Area
Aware Swin Transformer,
IJIST(33), No. 5, 2023, pp. 1549-1560.
DOI Link
2310
dermatological conditions classification, few-shot learning, transformer
BibRef
Song, Z.[Ze],
Kang, X.D.[Xu-Dong],
Wei, X.H.[Xiao-Hui],
Liu, H.B.[Hai-Bo],
Dian, R.[Renwei],
Li, S.T.[Shu-Tao],
FSNet: Focus Scanning Network for Camouflaged Object Detection,
IP(32), 2023, pp. 2267-2278.
IEEE DOI
2305
Transformers, Task analysis, Object detection,
Image color analysis, Charge coupled devices,
swin transformer
BibRef
Xu, Y.X.[Yi-Xuan],
Wang, X.B.[Xian-Bing],
Zhang, H.[Hua],
Lin, H.[Hai],
SE-Swin: An improved Swin-Transfomer network of self-ensemble feature
extraction framework for image retrieval,
IET-IPR(18), No. 1, 2024, pp. 13-21.
DOI Link
2401
Shifted Windows.
Swin-Transformer, Self-ensemble Feature Extraction, Feature Map Visualization
BibRef
Yang, R.P.[Rui-Ping],
Liu, K.[Kun],
Liang, Y.Q.[Yong-Quan],
A fusion-attention swin transformer for cardiac MRI image
segmentation,
IET-IPR(18), No. 1, 2024, pp. 105-115.
DOI Link
2401
biomedical magnetic resonance imaging, image segmentation,
medical image processing
BibRef
Liu, Y.H.[Yu-Hong],
Chen, S.[Shengbo],
Lei, Z.[Zhou],
Wang, P.[Peng],
Energy Consumption Optimization of Swin Transformer Based on Local
Aggregation and Group-Wise Transformation,
CVIDL23(463-471)
IEEE DOI
2403
Energy consumption, Adaptation models,
Computational modeling, Production, Transformers,
deep learning energy consumption
BibRef
Ning, J.[Jia],
Li, C.[Chen],
Zhang, Z.[Zheng],
Wang, C.Y.[Chun-Yu],
Geng, Z.[Zigang],
Dai, Q.[Qi],
He, K.[Kun],
Hu, H.[Han],
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token,
ICCV23(19843-19853)
IEEE DOI Code:
WWW Link.
2401
BibRef
Giroux, J.[James],
Bouchard, M.[Martin],
Laganière, R.[Robert],
T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw
ADC Radar Signals,
BRAVO23(4032-4041)
IEEE DOI
2401
BibRef
Tang, S.[Song],
Li, C.[Chuang],
Zhang, P.[Pu],
Tang, R.N.[Rong-Nian],
SwinLSTM: Improving Spatiotemporal Prediction Accuracy using Swin
Transformer and LSTM,
ICCV23(13424-13433)
IEEE DOI Code:
WWW Link.
2401
BibRef
Athwale, A.[Akshaya],
Afrasiyabi, A.[Arman],
Lagüe, J.[Justin],
Shili, I.[Ichrak],
Ahmad, O.[Ola],
Lalonde, J.F.[Jean-François],
DarSwin: Distortion Aware Radial Swin Transformer,
ICCV23(5906-5915)
IEEE DOI Code:
WWW Link.
2401
BibRef
Zeng, C.X.[Cheng-Xi],
Yang, X.Y.[Xin-Yu],
Smithard, D.[David],
Mirmehdi, M.[Majid],
Gambaruto, A.M.[Alberto M],
Burghardt, T.[Tilo],
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS
Instance Segmentation,
ICIP23(2470-2474)
IEEE DOI Code:
WWW Link.
2312
BibRef
Hong, S.H.[Sung-Hwan],
Cho, S.[Seokju],
Nam, J.[Jisu],
Lin, S.[Stephen],
Kim, S.[Seungryong],
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot
Segmentation,
ECCV22(XXIX:108-126).
Springer DOI
2211
BibRef
Liu, Z.[Ze],
Lin, Y.T.[Yu-Tong],
Cao, Y.[Yue],
Hu, H.[Han],
Wei, Y.X.[Yi-Xuan],
Zhang, Z.[Zheng],
Lin, S.[Stephen],
Guo, B.N.[Bai-Ning],
Swin Transformer: Hierarchical Vision Transformer using Shifted
Windows,
ICCV21(9992-10002)
IEEE DOI
WWW Link.
DOI Link
2203
Award, Marr Prize. Image segmentation, Visualization, Computational modeling,
Semantics, Object detection, Computer architecture,
grouping and shape
BibRef
Swin-Transformer-Object-Detection,
Online2021.
WWW Link.
Code, Swin Transform.
BibRef
2100
Cao, H.[Hu],
Wang, Y.[Yueyue],
Chen, J.[Joy],
Jiang, D.S.[Dong-Sheng],
Zhang, X.P.[Xiao-Peng],
Tian, Q.[Qi],
Wang, M.[Manning],
Swin-unet: Unet-like Pure Transformer for Medical Image Segmentation,
MCV22(205-218).
Springer DOI
2304
BibRef
Wang, Z.Y.[Zi-Yang],
Su, M.[Meiwen],
Zheng, J.Q.[Jian-Qing],
Liu, Y.[Yang],
Densely Connected Swin-UNet for Multiscale Information Aggregation in
Medical Image Segmentation,
ICIP23(940-944)
IEEE DOI Code:
WWW Link.
2312
BibRef
Ling, Z.X.[Zhi-Xin],
Xing, Z.[Zhen],
Zhou, X.D.[Xiang-Dong],
Cao, M.L.[Man-Liang],
Zhou, G.C.[Gui-Chun],
PanoSwin: a Pano-style Swin Transformer for Panorama Understanding,
CVPR23(17755-17764)
IEEE DOI
2309
BibRef
Wang, Y.X.[Yan-Xue],
Wang, S.S.[Shan-Song],
Ni, W.J.[Wei-Jian],
Zeng, Q.T.[Qing-Tian],
An Instance Segmentation Method for Anthracnose Based on Swin
Transformer and Path Aggregation,
ICIVC22(381-386)
IEEE DOI
2301
Image segmentation, Shape, Transfer learning, Crops, Transformers,
Feature extraction, Lesions, Swin Transformer, PANet, anthracnose,
instance segmentation
BibRef
Li, B.C.[Bing-Chen],
Li, X.[Xin],
Lu, Y.T.[Yi-Ting],
Liu, S.[Sen],
Feng, R.[Ruoyu],
Chen, Z.B.[Zhi-Bo],
HST: Hierarchical Swin Transformer for Compressed Image
Super-resolution,
AIM22(651-668).
Springer DOI
2304
BibRef
Conde, M.V.[Marcos V.],
Choi, U.J.[Ui-Jin],
Burchi, M.[Maxime],
Timofte, R.[Radu],
Swin2sr: Swinv2 Transformer for Compressed Image Super-resolution and
Restoration,
AIM22(669-687).
Springer DOI
2304
BibRef
Liu, Z.[Ze],
Hu, H.[Han],
Lin, Y.T.[Yu-Tong],
Yao, Z.L.[Zhu-Liang],
Xie, Z.D.[Zhen-Da],
Wei, Y.X.[Yi-Xuan],
Ning, J.[Jia],
Cao, Y.[Yue],
Zhang, Z.[Zheng],
Dong, L.[Li],
Wei, F.[Furu],
Guo, B.[Baining],
Swin Transformer V2: Scaling Up Capacity and Resolution,
CVPR22(11999-12009)
IEEE DOI
2210
Training, Representation learning, Adaptation models,
Image resolution, Computational modeling, Semantics,
Representation learning
BibRef
Dong, X.Y.[Xiao-Yi],
Bao, J.M.[Jian-Min],
Chen, D.D.[Dong-Dong],
Zhang, W.M.[Wei-Ming],
Yu, N.H.[Neng-Hai],
Yuan, L.[Lu],
Chen, D.[Dong],
Guo, B.[Baining],
CSWin Transformer: A General Vision Transformer Backbone with
Cross-Shaped Windows,
CVPR22(12114-12124)
IEEE DOI
2210
Image segmentation, Costs, Mathematical analysis, Training data,
Transformer cores, Transformers,
grouping and shape analysis
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Semi-Supervised Object Detection .