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Luo, C.[Chang],
Huang, H.Q.[Han-Qiao],
Zhao, H.Z.[Hui-Zhen],
Wang, S.Q.[Shi-Qiang],
Transferring Pre-Trained Deep CNNs for Remote Scene Classification
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RS(9), No. 3, 2017, pp. xx-yy.
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BibRef
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Elsevier DOI
2107
Transfer learning, Medical image analysis,
Convolutional neural network, Survival prediction
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Nakamura, A.[Akio],
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Pre-Training Without Natural Images,
IJCV(130), No. 1, January 2022, pp. 990-1007.
Springer DOI
2204
BibRef
Earlier:
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Springer DOI
2103
BibRef
Xu, C.[Cong],
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Yang, M.[Min],
Adversarial momentum-contrastive pre-training,
PRL(160), 2022, pp. 172-179.
Elsevier DOI
2208
Real samples and adversarial samples for training.
Adversarial robustness, Contrastive learning, Memory bank, Fine-tuning
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Lu, C.X.[Chi-Xiang],
Chen, C.Q.[Chao-Qi],
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A Unified Visual Information Preservation Framework for
Self-supervised Pre-Training in Medical Image Analysis,
PAMI(45), No. 7, July 2023, pp. 8020-8035.
IEEE DOI
2306
Semantics, Image restoration, Task analysis, Visualization,
Medical diagnostic imaging, Image segmentation,
transfer learning
BibRef
Chen, Z.H.[Zi-Han],
Zhu, H.Y.[Hong-Yuan],
Cheng, H.[Hao],
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PR(135), 2023, pp. 109185.
Elsevier DOI
2212
Self-supervised learning, Contrastive learning, Dense representation
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PR(139), 2023, pp. 109437.
Elsevier DOI
2304
Visual pre-training, Self-supervised learning, Multi-grained visual information
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Peng, J.[Junran],
Chang, Q.[Qing],
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Bu, X.Y.[Xing-Yuan],
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Xie, L.X.[Ling-Xi],
Zhang, X.P.[Xiao-Peng],
Tian, Q.[Qi],
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IEEE DOI
2310
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Chu, W.[Wei],
SNP-S3: Shared Network Pre-Training and Significant Semantic
Strengthening for Various Video-Text Tasks,
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WWW Link.
2404
Task analysis, Visualization, Feature extraction, Semantics,
Training, Transformers, Video-text pre-training, video-text matching
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Self-supervised Pre-training for Mirror Detection,
ICCV23(12193-12202)
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WWW Link.
2401
BibRef
Zha, Y.[Yaohua],
Wang, J.P.[Jin-Peng],
Dai, T.[Tao],
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Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud
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ICCV23(14115-14124)
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2401
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Kil, J.[Jihyung],
Changpinyo, S.[Soravit],
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Soricut, R.[Radu],
PreSTU: Pre-Training for Scene-Text Understanding,
ICCV23(15224-15234)
IEEE DOI
2401
BibRef
Huang, D.[Di],
Peng, S.[Sida],
He, T.[Tong],
Yang, H.H.[Hong-Hui],
Zhou, X.W.[Xiao-Wei],
Ouyang, W.L.[Wan-Li],
Ponder: Point Cloud Pre-training via Neural Rendering,
ICCV23(16043-16052)
IEEE DOI
2401
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Mendieta, M.[Matías],
Han, B.[Boran],
Shi, X.J.[Xing-Jian],
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Towards Geospatial Foundation Models via Continual Pretraining,
ICCV23(16760-16770)
IEEE DOI Code:
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2401
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Zhou, J.B.[Jing-Bo],
Tuning Pre-trained Model via Moment Probing,
ICCV23(11769-11779)
IEEE DOI
2401
BibRef
Wang, J.R.[Jian-Ren],
Dasari, S.[Sudeep],
Srirama, M.K.[Mohan Kumar],
Tulsiani, S.[Shubham],
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Manipulate by Seeing: Creating Manipulation Controllers from
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ICCV23(3836-3845)
IEEE DOI Code:
WWW Link.
2401
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Wang, Z.J.[Zi-Jian],
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How Far Pre-trained Models Are from Neural Collapse on the Target
Dataset Informs their Transferability,
ICCV23(5526-5535)
IEEE DOI
2401
BibRef
Jain, N.[Nishant],
Behl, H.[Harkirat],
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Efficiently Robustify Pre-Trained Models,
ICCV23(5482-5492)
IEEE DOI
2401
BibRef
Kim, B.[Bumsoo],
Jo, Y.[Yeonsik],
Kim, J.[Jinhyung],
Kim, S.[Seunghwan],
Misalign, Contrast then Distill:
Rethinking Misalignments in Language-Image Pretraining,
ICCV23(2563-2572)
IEEE DOI
2401
BibRef
Wang, A.[Angelina],
Russakovsky, O.[Olga],
Overwriting Pretrained Bias with Finetuning Data,
ICCV23(3934-3945)
IEEE DOI
2401
BibRef
Chavhan, R.[Ruchika],
Gouk, H.[Henry],
Li, D.[Da],
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Quality Diversity for Visual Pre-Training,
ICCV23(5361-5371)
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2401
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Adcock, A.[Aaron],
Joulin, A.[Armand],
Dollár, P.[Piotr],
Feichtenhofer, C.[Christoph],
Girshick, R.[Ross],
Girdhar, R.[Rohit],
Misra, I.[Ishan],
The effectiveness of MAE pre-pretraining for billion-scale
pretraining,
ICCV23(5461-5471)
IEEE DOI
2401
BibRef
Fu, C.[Cheng],
Huang, H.[Hanxian],
Jiang, Z.X.[Zi-Xuan],
Ni, Y.[Yun],
Nai, L.F.[Li-Feng],
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Cheng, L.Q.[Li-Qun],
Zhou, Y.Q.[Yan-Qi],
Li, S.[Sheng],
Li, A.[Andrew],
Zhao, J.[Jishen],
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning
for Efficient Vision Transformer Scaling and Searching,
ICCV23(17107-17117)
IEEE DOI
2401
BibRef
Li, D.Q.[Dai-Qing],
Ling, H.[Huan],
Kar, A.[Amlan],
Acuna, D.[David],
Kim, S.W.[Seung Wook],
Kreis, K.[Karsten],
Torralba, A.[Antonio],
Fidler, S.[Sanja],
DreamTeacher: Pretraining Image Backbones with Deep Generative Models,
ICCV23(16652-16662)
IEEE DOI
2401
BibRef
Lew, B.G.[Byoung-Gyu],
Son, D.H.[Dong-Hyun],
Chang, B.[Buru],
Gradient Estimation for Unseen Domain Risk Minimization with
Pre-Trained Models,
OutDistri23(4438-4448)
IEEE DOI
2401
BibRef
Liu, S.[Sheng],
Huynh, C.P.[Cong Phuoc],
Chen, C.[Cong],
Arap, M.[Maxim],
Hamid, R.[Raffay],
LEMaRT: Label-Efficient Masked Region Transform for Image
Harmonization,
CVPR23(18290-18299)
IEEE DOI
2309
BibRef
Wang, Y.M.[Yao-Ming],
Shi, B.[Bowen],
Zhang, X.P.[Xiao-Peng],
Li, J.[Jin],
Liu, Y.C.[Yu-Chen],
Dai, W.R.[Wen-Rui],
Li, C.L.[Cheng-Lin],
Xiong, H.K.[Hong-Kai],
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CVPR23(15965-15974)
IEEE DOI
2309
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Zhang, D.D.[Dong-Dong],
Duan, N.[Nan],
M3P: Learning Universal Representations via Multitask Multilingual
Multimodal Pre-training,
CVPR21(3976-3985)
IEEE DOI
2111
Training, Computational modeling, Semantics,
Image retrieval, Benchmark testing, Data models
BibRef
Li, T.J.[Tian-Jiao],
Foo, L.G.[Lin Geng],
Hu, P.[Ping],
Shang, X.[Xindi],
Rahmani, H.[Hossein],
Yuan, Z.H.[Ze-Huan],
Liu, J.[Jun],
Token Boosting for Robust Self-Supervised Visual Transformer
Pre-training,
CVPR23(24027-24038)
IEEE DOI
2309
BibRef
Yan, X.Y.[Xiang-Yi],
Naushad, J.[Junayed],
Sun, S.L.[Shan-Lin],
Han, K.[Kun],
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Ma, H.Y.[Hao-Yu],
You, C.Y.[Chen-Yu],
Xie, X.H.[Xiao-Hui],
Representation Recovering for Self-Supervised Pre-training on Medical
Images,
WACV23(2684-2694)
IEEE DOI
2302
Representation learning, Visualization, Image segmentation,
Semantics, Self-supervised learning, Feature extraction
BibRef
Lee, K.Y.[Kuan-Ying],
Zhong, Y.[Yuanyi],
Wang, Y.X.[Yu-Xiong],
Do Pre-trained Models Benefit Equally in Continual Learning?,
WACV23(6474-6482)
IEEE DOI
2302
Training, Systematics, Codes, Computational modeling, Pipelines,
Benchmark testing, Algorithms: Machine learning architectures,
and algorithms (including transfer)
BibRef
Su, W.J.[Wei-Jie],
Zhu, X.[Xizhou],
Tao, C.X.[Chen-Xin],
Lu, L.W.[Le-Wei],
Li, B.[Bin],
Huang, G.[Gao],
Qiao, Y.[Yu],
Wang, X.G.[Xiao-Gang],
Zhou, J.[Jie],
Dai, J.F.[Ji-Feng],
Towards All-in-One Pre-Training via Maximizing Multi-Modal Mutual
Information,
CVPR23(15888-15899)
IEEE DOI
2309
BibRef
Wei, L.[Longhui],
Xie, L.X.[Ling-Xi],
Zhou, W.G.[Wen-Gang],
Li, H.Q.[Hou-Qiang],
Tian, Q.[Qi],
MVP: Multimodality-Guided Visual Pre-training,
ECCV22(XXX:337-353).
Springer DOI
2211
BibRef
Yuan, Z.W.[Zhuo-Wen],
Wu, F.[Fan],
Long, Y.H.[Yun-Hui],
Xiao, C.W.[Chao-Wei],
Li, B.[Bo],
SecretGen: Privacy Recovery on Pre-trained Models via Distribution
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ECCV22(V:139-155).
Springer DOI
2211
BibRef
Yang, J.W.[Jia-Wei],
Chen, H.[Hanbo],
Liang, Y.[Yuan],
Huang, J.Z.[Jun-Zhou],
He, L.[Lei],
Yao, J.H.[Jian-Hua],
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training
in Pathology Images,
ECCV22(XXI:523-539).
Springer DOI
2211
BibRef
You, H.X.[Hao-Xuan],
Zhou, L.W.[Luo-Wei],
Xiao, B.[Bin],
Codella, N.[Noel],
Cheng, Y.[Yu],
Xu, R.C.[Ruo-Chen],
Chang, S.F.[Shih-Fu],
Yuan, L.[Lu],
Learning Visual Representation from Modality-Shared Contrastive
Language-Image Pre-training,
ECCV22(XXVII:69-87).
Springer DOI
2211
BibRef
Chakraborty, S.[Shuvam],
Uzkent, B.[Burak],
Ayush, K.[Kumar],
Tanmay, K.[Kumar],
Sheehan, E.[Evan],
Ermon, S.[Stefano],
Efficient Conditional Pre-training for Transfer Learning,
L3D-IVU22(4240-4249)
IEEE DOI
2210
Training, Costs, Image resolution, Filtering, Computational modeling,
Transfer learning
BibRef
Li, Z.W.[Zhao-Wen],
Zhu, Y.S.[You-Song],
Yang, F.[Fan],
Li, W.[Wei],
Zhao, C.Y.[Chao-Yang],
Chen, Y.Y.[Ying-Ying],
Chen, Z.Y.[Zhi-Yang],
Xie, J.H.[Jia-Hao],
Wu, L.W.[Li-Wei],
Zhao, R.[Rui],
Tang, M.[Ming],
Wang, J.Q.[Jin-Qiao],
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training,
CVPR22(14607-14616)
IEEE DOI
2210
Representation learning, Visualization, Image segmentation,
Correlation, Semantics, Self-supervised learning, Object detection,
Transfer/low-shot/long-tail learning
BibRef
Li, W.[Wei],
Xie, J.H.[Jia-Hao],
Loy, C.C.[Chen Change],
Correlational Image Modeling for Self-Supervised Visual Pre-Training,
CVPR23(15105-15115)
IEEE DOI
2309
BibRef
Jia, M.L.[Meng-Lin],
Tang, L.[Luming],
Chen, B.C.[Bor-Chun],
Cardie, C.[Claire],
Belongie, S.[Serge],
Hariharan, B.[Bharath],
Lim, S.N.[Ser-Nam],
Visual Prompt Tuning,
ECCV22(XXXIII:709-727).
Springer DOI
2211
WWW Link. Adapt pre-trainted model
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Xu, C.F.[Chen-Feng],
Li, T.[Tian],
Tang, C.[Chen],
Sun, L.F.[Ling-Feng],
Keutzer, K.[Kurt],
Tomizuka, M.[Masayoshi],
Fathi, A.[Alireza],
Zhan, W.[Wei],
PreTraM: Self-supervised Pre-training via Connecting Trajectory and Map,
ECCV22(XXIX:34-50).
Springer DOI
2211
BibRef
Wei, C.[Chen],
Fan, H.Q.[Hao-Qi],
Xie, S.[Saining],
Wu, C.Y.[Chao-Yuan],
Yuille, A.L.[Alan L.],
Feichtenhofer, C.[Christoph],
Masked Feature Prediction for Self-Supervised Visual Pre-Training,
CVPR22(14648-14658)
IEEE DOI
2210
Deep learning, Visualization, Histograms, Computational modeling,
Transfer learning, Predictive models, Video analysis and understanding
BibRef
Mishra, S.[Samarth],
Panda, R.[Rameswar],
Phoo, C.P.[Cheng Perng],
Chen, C.F.R.[Chun-Fu Richard],
Karlinsky, L.[Leonid],
Saenko, K.[Kate],
Saligrama, V.[Venkatesh],
Feris, R.S.[Rogerio S.],
Task2Sim: Towards Effective Pre-training and Transfer from Synthetic
Data,
CVPR22(9184-9194)
IEEE DOI
2210
Graphics, Training, Representation learning, Adaptation models,
Computational modeling, Data models, retrieval
BibRef
Singh, M.[Mannat],
Gustafson, L.[Laura],
Adcock, A.[Aaron],
de Freitas-Reis, V.[Vinicius],
Gedik, B.[Bugra],
Kosaraju, R.P.[Raj Prateek],
Mahajan, D.[Dhruv],
Girshick, R.[Ross],
Dollár, P.[Piotr],
van der Maaten, L.[Laurens],
Revisiting Weakly Supervised Pre-Training of Visual Perception Models,
CVPR22(794-804)
IEEE DOI
2210
Visualization, Computational modeling, Supervised learning,
Self-supervised learning, Pattern recognition, Standards,
Transfer/low-shot/long-tail learning
BibRef
Cha, J.[Junbum],
Lee, K.[Kyungjae],
Park, S.[Sungrae],
Chun, S.[Sanghyuk],
Domain Generalization by Mutual-Information Regularization with
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ECCV22(XXIII:440-457).
Springer DOI
2211
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Kim, D.H.[Dong-Hyun],
Wang, K.[Kaihong],
Sclaroff, S.[Stan],
Saenko, K.[Kate],
A Broad Study of Pre-training for Domain Generalization and Adaptation,
ECCV22(XXXIII:621-638).
Springer DOI
2211
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Zhu, X.Z.[Xi-Zhou],
Zhu, J.[Jinguo],
Li, H.[Hao],
Wu, X.S.[Xiao-Shi],
Li, H.S.[Hong-Sheng],
Wang, X.H.[Xiao-Hua],
Dai, J.F.[Ji-Feng],
Uni-Perceiver: Pre-training Unified Architecture for Generic
Perception for Zero-shot and Few-shot Tasks,
CVPR22(16783-16794)
IEEE DOI
2210
Representation learning, Costs, Collaboration,
Transformers, Data models,
BibRef
Wang, X.L.[Xin-Long],
Zhang, R.F.[Ru-Feng],
Shen, C.H.[Chun-Hua],
Kong, T.[Tao],
Li, L.[Lei],
Dense Contrastive Learning for Self-Supervised Visual Pre-Training,
CVPR21(3023-3032)
IEEE DOI
2111
Learning systems, Image segmentation,
Visualization, Computational modeling, Semantics, Object detection
BibRef
Mañas, O.[Oscar],
Lacoste, A.[Alexandre],
Giró-i-Nieto, X.[Xavier],
Vazquez, D.[David],
Rodríguez, P.[Pau],
Seasonal Contrast:
Unsupervised Pre-Training from Uncurated Remote Sensing Data,
ICCV21(9394-9403)
IEEE DOI
2203
Earth, Deep learning, Satellites, Transfer learning, Pipelines,
Supervised learning, Data models, Vision applications and systems
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Zhang, Y.[Youshan],
Davison, B.D.[Brian D.],
Efficient Pre-trained Features and Recurrent Pseudo-Labeling in
Unsupervised Domain Adaptation,
LLID21(2713-2722)
IEEE DOI
2109
Training, Adaptation models, Computational modeling, Benchmark testing
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Chowdhury, A.[Arkabandhu],
Jiang, M.C.[Ming-Chao],
Chaudhuri, S.[Swarat],
Jermaine, C.[Chris],
Few-shot Image Classification: Just Use a Library of Pre-trained
Feature Extractors and a Simple Classifier,
ICCV21(9425-9434)
IEEE DOI
2203
Transfer learning, Feature extraction, Libraries,
Computational efficiency, Classification algorithms, Feeds,
Vision applications and systems
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Kim, D.H.[Dong-Hyun],
Saito, K.[Kuniaki],
Oh, T.H.[Tae-Hyun],
Plummer, B.A.[Bryan A.],
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Saenko, K.[Kate],
CDS: Cross-Domain Self-supervised Pre-training,
ICCV21(9103-9112)
IEEE DOI
2203
Transfer learning, Task analysis, Standards,
Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning
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Zhang, J.O.[Jeffrey O.],
Sax, A.[Alexander],
Zamir, A.[Amir],
Guibas, L.J.[Leonidas J.],
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Side-Tuning:
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ECCV20(III:698-714).
Springer DOI
2012
Adapt pre-trained network, not start from beginning.
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Yan, X.T.[Xue-Ting],
Misra, I.[Ishan],
Gupta, A.[Abhinav],
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Mahajan, D.[Dhruv],
ClusterFit: Improving Generalization of Visual Representations,
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IEEE DOI
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Pre-training.
Task analysis, Training, Feature extraction, Visualization, Videos,
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The Impact of Padding on Image Classification by Using Pre-trained
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1909
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Chakraborty, R.,
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A Mixture Model for Aggregation of Multiple Pre-Trained Weak
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Diff-CVML18(454-4547)
IEEE DOI
1812
Feature extraction, Training, Frequency modulation, Boosting,
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BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Domain Adaptation .