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Luo, C.[Chang],
Huang, H.Q.[Han-Qiao],
Zhao, H.Z.[Hui-Zhen],
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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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JVCIR(78), 2021, pp. 103145.
Elsevier DOI
2107
Transfer learning, Medical image analysis,
Convolutional neural network, Survival prediction
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Zhuang, Y.[Yin],
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Chen, H.[He],
Consecutive Pre-Training: A Knowledge Transfer Learning Strategy with
Relevant Unlabeled Data for Remote Sensing Domain,
RS(14), No. 22, 2022, pp. xx-yy.
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Kataoka, H.[Hirokatsu],
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Pre-Training Without Natural Images,
IJCV(130), No. 1, January 2022, pp. 990-1007.
Springer DOI
2204
BibRef
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Springer DOI
2103
BibRef
Xu, C.[Cong],
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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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Chen, C.Q.[Chao-Qi],
Yang, S.[Sibei],
Yu, Y.Z.[Yi-Zhou],
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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dense visual pre-training,
PR(135), 2023, pp. 109185.
Elsevier DOI
2212
Self-supervised learning, Contrastive learning, Dense representation
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Zhou, W.G.[Wen-Gang],
Li, H.Q.[Hou-Qiang],
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Exploring the diversity and invariance in yourself for visual
pre-training task,
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],
Yin, H.R.[Hao-Ran],
Bu, X.Y.[Xing-Yuan],
Sun, J.J.[Jia-Jun],
Xie, L.X.[Ling-Xi],
Zhang, X.P.[Xiao-Peng],
Tian, Q.[Qi],
Zhang, Z.X.[Zhao-Xiang],
GAIA-Universe: Everything is Super-Netify,
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IEEE DOI
2310
WWW Link.
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Dong, X.N.[Xing-Ning],
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Ren, X.Y.[Xiang-Yuan],
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Chu, W.[Wei],
SNP-S3: Shared Network Pre-Training and Significant Semantic
Strengthening for Various Video-Text Tasks,
CirSysVideo(34), No. 4, April 2024, pp. 2525-2535.
IEEE DOI Code:
WWW Link.
2404
Task analysis, Visualization, Feature extraction, Semantics,
Training, Transformers, Video-text pre-training, video-text matching
BibRef
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Liu, P.[Peng],
Lee, K.[Kyusong],
OmDet: Large-scale vision-language multi-dataset pre-training with
multimodal detection network,
IET-CV(18), No. 5, 2024, pp. 626-639.
DOI Link
2408
object detection, object recognition
BibRef
Tang, Y.[Yuan],
Li, X.Z.[Xian-Zhi],
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Hu, L.[Long],
Hao, Y.X.[Yi-Xue],
Chen, M.[Min],
Point-LGMask: Local and Global Contexts Embedding for Point Cloud
Pre-Training With Multi-Ratio Masking,
MultMed(26), 2024, pp. 8360-8370.
IEEE DOI
2408
Point cloud compression, Task analysis, Predictive models,
Self-supervised learning, Representation learning,
representation learning
BibRef
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Chang, J.L.[Jian-Long],
Wang, H.X.[Hai-Xin],
Liu, L.B.[Ling-Bo],
Wang, S.J.[Shi-Jie],
Wang, Z.Y.[Zhi-Yu],
Lin, J.F.[Jun-Fan],
Xie, L.X.[Ling-Xi],
Li, H.J.[Hao-Jie],
Lin, Z.C.[Zhou-Chen],
Tian, Q.[Qi],
Chen, C.W.[Chang Wen],
Visual Tuning,
Surveys(56), No. 12, July 2024, pp. xx-yy.
DOI Link
2410
Foundation model, fine-tuning, parameter-efficient, pre-training
BibRef
Huang, Y.[Yipo],
Li, L.[Leida],
Chen, P.F.[Peng-Fei],
Wu, H.N.[Hao-Ning],
Lin, W.S.[Wei-Si],
Shi, G.M.[Guang-Ming],
Multi-Modality Multi-Attribute Contrastive Pre-Training for Image
Aesthetics Computing,
PAMI(47), No. 2, February 2025, pp. 1205-1218.
IEEE DOI
2501
Computational modeling, Databases, Image color analysis, Lighting,
Contrastive learning, Visualization, Semantics, Reviews,
aesthetic representation
BibRef
Baraldi, L.[Lorenzo],
Amoroso, R.[Roberto],
Cornia, M.[Marcella],
Baraldi, L.[Lorenzo],
Pilzer, A.[Andrea],
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CVIU(252), 2025, pp. 104294.
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WWW Link.
2502
BibRef
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Herde, M.[Marek],
Hahn, P.[Paul],
Müjde, M.[Mehmet],
Sick, B.[Bernhard],
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Detectors,
IJCV(133), No. 3, March 2025, pp. 1033-1047.
Springer DOI
2502
BibRef
Tian, Y.J.[Yun-Jie],
Xie, L.X.[Ling-Xi],
Fang, J.[Jiemin],
Jiao, J.B.[Jian-Bin],
Tian, Q.[Qi],
Beyond masking: Demystifying token-based pre-training for vision
transformers,
PR(162), 2025, pp. 111386.
Elsevier DOI
2503
Self-supervised learning, Vision transformers,
Token-based pre-training, Masked image modeling
BibRef
Huang, L.[Lan],
Zeng, J.[Jia],
Yu, M.Q.[Meng-Qiang],
Ding, W.P.[Wei-Ping],
Bai, X.Y.[Xing-Yu],
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CVIU(254), 2025, pp. 104326.
Elsevier DOI Code:
WWW Link.
2503
Feature selection, Vision transformer, Model pruning
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Feng, T.[Tuo],
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Shape2scene: 3d Scene Representation Learning Through Pre-training on
Shape Data,
ECCV24(LV: 73-91).
Springer DOI
2412
BibRef
Tang, Y.W.[Yi-Wen],
Zhang, R.[Ray],
Liu, J.[JiaMing],
Guo, Z.[Zoey],
Zhao, B.[Bin],
Wang, Z.G.[Zhi-Gang],
Gao, P.[Peng],
Li, H.S.[Hong-Sheng],
Wang, D.[Dong],
Li, X.L.[Xue-Long],
Any2point: Empowering Any-modality Large Models for Efficient 3d
Understanding,
ECCV24(XXXVI: 456-473).
Springer DOI
2412
Adapt pre-trained 2d to 3d.
Code:
WWW Link.
BibRef
Zheng, M.Y.[Meng-Yu],
Hao, Z.W.[Zhi-Wei],
Tang, Y.[Yehui],
Xu, C.[Chang],
Visual Prompting via Partial Optimal Transport,
ECCV24(XXXV: 1-18).
Springer DOI
2412
BibRef
Wu, S.[Shuchi],
Ma, C.[Chuan],
Wei, K.[Kang],
Xu, X.G.[Xiao-Gang],
Ding, M.[Ming],
Qian, Y.[Yuwen],
Xiao, D.[Di],
Xiang, T.[Tao],
Refine, Discriminate and Align: Stealing Encoders via Sample-wise
Prototypes and Multi-relational Extraction,
ECCV24(XXXIV: 186-203).
Springer DOI
2412
Code:
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BibRef
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Park, H.[Hyejin],
Yi, K.M.[Kwang Moo],
Cha, S.[Sungmin],
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Salience-based Adaptive Masking:
Revisiting Token Dynamics for Enhanced Pre-Training,
ECCV24(LXXVIII: 343-359).
Springer DOI
2412
BibRef
Huynh, A.V.[Andy V.],
Gillespie, L.E.[Lauren E.],
Lopez-Saucedo, J.[Jael],
Tang, C.[Claire],
Sikand, R.[Rohan],
Expósito-Alonso, M.[Moisés],
Contrastive Ground-level Image and Remote Sensing Pre-training Improves
Representation Learning for Natural World Imagery,
ECCV24(LXXX: 173-190).
Springer DOI
2412
BibRef
Luo, H.[Hao],
Zhou, B.[Bohan],
Lu, Z.Q.[Zong-Qing],
Pre-trained Visual Dynamics Representations for Efficient Policy
Learning,
ECCV24(LXXXI: 249-267).
Springer DOI
2412
BibRef
Choi, H.[Hyesong],
Lee, H.[Hunsang],
Joung, S.[Seyoung],
Park, H.[Hyejin],
Kim, J.Y.[Ji-Yeong],
Min, D.B.[Dong-Bo],
Emerging Property of Masked Token for Effective Pre-training,
ECCV24(LXXVI: 272-289).
Springer DOI
2412
BibRef
Zhang, Y.Y.[Ying-Ying],
Guo, X.[Xin],
Lao, J.W.[Jiang-Wei],
Yu, L.[Lei],
Ru, L.X.[Li-Xiang],
Wang, J.[Jian],
Ye, G.[Guo],
He, H.M.[Hui-Mei],
Chen, J.D.[Jing-Dong],
Yang, M.[Ming],
POA: Pre-training Once for Models of All Sizes,
ECCV24(III: 131-148).
Springer DOI
2412
BibRef
Nakamura, R.[Ryo],
Tadokoro, R.[Ryu],
Yamada, R.[Ryosuke],
Asano, Y.M.[Yuki M.],
Laina, I.[Iro],
Rupprecht, C.[Christian],
Inoue, N.[Nakamasa],
Yokota, R.[Rio],
Kataoka, H.[Hirokatsu],
Scaling Backwards: Minimal Synthetic Pre-Training?,
ECCV24(XV: 153-171).
Springer DOI
2412
BibRef
Yamada, R.[Ryosuke],
Hara, K.[Kensho],
Kataoka, H.[Hirokatsu],
Makihara, K.[Koshi],
Inoue, N.[Nakamasa],
Yokota, R.[Rio],
Satoh, Y.[Yutaka],
Formula-Supervised Visual-Geometric Pre-Training,
ECCV24(XXII: 57-74).
Springer DOI
2412
BibRef
Zhang, L.[Lixuan],
Kan, M.[Meina],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Prelar: World Model Pre-Training with Learnable Action Representation,
ECCV24(XXIII: 185-201).
Springer DOI
2412
BibRef
Yang, M.Y.[Meng-Yu],
Tian, Y.[Ye],
Zhang, L.[Lanshan],
Liang, X.[Xiao],
Ran, X.M.[Xu-Ming],
Wang, W.D.[Wen-Dong],
AdaViPro: Region-Based Adaptive Visual Prompt for Large-Scale Models
Adapting,
ICIP24(1316-1322)
IEEE DOI
2411
Training, Adaptation models, Visualization, Image resolution,
Accuracy, Decision making, Benchmark testing
BibRef
Li, X.[Xiang],
Togo, R.[Ren],
Maeda, K.[Keisuke],
Ogawa, T.[Takahiro],
Haseyama, M.[Miki],
Reinforcing Pre-Trained Models Using Counterfactual Images,
ICIP24(486-492)
IEEE DOI
2411
Deep learning, Training, Image recognition, Decision making,
Data augmentation, Robustness, Data models, Deep learning
BibRef
Marathe, K.[Kalyani],
Bigverdi, M.[Mahtab],
Khan, N.[Nishat],
Kundu, T.[Tuhin],
Howe, P.[Patrick],
Ranjit, S.S.[S. Sharan],
Bhattad, A.[Anand],
Kembhavi, A.[Aniruddha],
Shapiro, L.G.[Linda G.],
Krishna, R.[Ranjay],
MIMIC: Masked Image Modeling with Image Correspondences,
L3D24(718-727)
IEEE DOI
2410
Representation learning, Point cloud compression, Soft sensors,
Image edge detection, MIMICs, Buildings, Masked Image Modeling,
Dense representation learning
BibRef
Han, K.[Kai],
Wang, Y.H.[Yun-He],
Guo, J.[Jianyuan],
Wu, E.[Enhua],
ParameterNet: Parameters are All You Need for Large-Scale Visual
Pretraining of Mobile Networks,
CVPR24(15751-15761)
IEEE DOI Code:
WWW Link.
2410
Convolutional codes, Visualization, Accuracy, Transformers
BibRef
Zhao, Z.Y.[Zhi-Yu],
Huang, B.K.[Bing-Kun],
Xing, S.[Sen],
Wu, G.S.[Gang-Shan],
Qiao, Y.[Yu],
Wang, L.M.[Li-Min],
Asymmetric Masked Distillation for Pre-Training Small Foundation
Models,
CVPR24(18516-18526)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Accuracy, Image recognition,
Computational modeling, Transformer cores, Transformers
BibRef
Chiche, B.N.[Benjamin Naoto],
Horikawa, Y.[Yuto],
Fujita, R.[Ryo],
Pre-Training Vision Models with Mandelbulb Variations,
CVPR24(22062-22071)
IEEE DOI
2410
Training, Ethics, Accuracy, Licenses, Transformers,
Formula-driven supervised learning, pre-training, mandelbulb
BibRef
Miao, Y.[Yibo],
Lei, Y.[Yu],
Zhou, F.[Feng],
Deng, Z.J.[Zhi-Jie],
Bayesian Exploration of Pre-Trained Models for Low-Shot Image
Classification,
CVPR24(23849-23859)
IEEE DOI
2410
Uncertainty, Computational modeling, Probabilistic logic,
Robustness, Bayes methods, Kernel, low-shot, classification
BibRef
Noman, M.[Mubashir],
Naseer, M.[Muzammal],
Cholakkal, H.[Hisham],
Anwar, R.M.[Rao Muhammad],
Khan, S.[Salman],
Khan, F.S.[Fahad Shahbaz],
Rethinking Transformers Pre-training for Multi-Spectral Satellite
Imagery,
CVPR24(27811-27819)
IEEE DOI Code:
WWW Link.
2410
Image resolution, Transformers, Optical imaging, Satellite images,
Optical sensors, Remote sensing, multi-spectral imagery
BibRef
Obadic, I.[Ivica],
Levering, A.[Alex],
Pennig, L.[Lars],
Oliveira, D.[Dario],
Marcos, D.[Diego],
Zhu, X.X.[Xiao-Xiang],
Contrastive Pretraining for Visual Concept Explanations of
Socioeconomic Outcomes,
EarthVision24(575-584)
IEEE DOI
2410
Deep learning, Training, Visualization, Sensitivity,
Vegetation mapping, Predictive models, Vectors,
contrastive-pretraining
BibRef
Koch, S.[Sebastian],
Hermosilla, P.[Pedro],
Vaskevicius, N.[Narunas],
Colosi, M.[Mirco],
Ropinski, T.[Timo],
Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph
prediction,
3DV24(1037-1047)
IEEE DOI
2408
Training, Point cloud compression, Knowledge engineering,
Solid modeling, Semantics, Natural languages, 3D Scene Graph, GCN
BibRef
Sadhu, A.[Arka],
Nevatia, R.[Ram],
Leveraging Task-Specific Pre-Training to Reason across Images and
Videos,
WACV24(5782-5792)
IEEE DOI
2404
Visualization, Image recognition, Annotations, Focusing, Cognition,
Data models, Algorithms, Vision + language and/or other modalities
BibRef
Lin, J.Y.[Jia-Ying],
Lau, R.W.H.[Rynson W. H.],
Self-supervised Pre-training for Mirror Detection,
ICCV23(12193-12202)
IEEE DOI Code:
WWW Link.
2401
BibRef
Zha, Y.[Yaohua],
Wang, J.P.[Jin-Peng],
Dai, T.[Tao],
Chen, B.[Bin],
Wang, Z.[Zhi],
Xia, S.T.[Shu-Tao],
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud
Models,
ICCV23(14115-14124)
IEEE DOI Code:
WWW Link.
2401
BibRef
Kil, J.[Jihyung],
Changpinyo, S.[Soravit],
Chen, X.[Xi],
Hu, H.X.[He-Xiang],
Goodman, S.[Sebastian],
Chao, W.L.[Wei-Lun],
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
BibRef
Mendieta, M.[Matías],
Han, B.[Boran],
Shi, X.J.[Xing-Jian],
Zhu, Y.[Yi],
Chen, C.[Chen],
Towards Geospatial Foundation Models via Continual Pretraining,
ICCV23(16760-16770)
IEEE DOI Code:
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2401
BibRef
Gao, M.Z.[Ming-Ze],
Wang, Q.L.[Qi-Long],
Lin, Z.[Zhenyi],
Zhu, P.F.[Peng-Fei],
Hu, Q.H.[Qing-Hua],
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],
Gupta, A.[Abhinav],
Manipulate by Seeing: Creating Manipulation Controllers from
Pre-Trained Representations,
ICCV23(3836-3845)
IEEE DOI Code:
WWW Link.
2401
BibRef
Wang, Z.J.[Zi-Jian],
Luo, Y.[Yadan],
Zheng, L.[Liang],
Huang, Z.[Zi],
Baktashmotlagh, M.[Mahsa],
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],
Rawat, Y.S.[Yogesh Singh],
Vineet, V.[Vibhav],
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],
Hospedales, T.M.[Timothy M.],
Quality Diversity for Visual Pre-Training,
ICCV23(5361-5371)
IEEE DOI Code:
WWW Link.
2401
BibRef
Singh, M.[Mannat],
Duval, Q.[Quentin],
Alwala, K.V.[Kalyan Vasudev],
Fan, H.Q.[Hao-Qi],
Aggarwal, V.[Vaibhav],
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.X.[Han-Xian],
Jiang, Z.X.[Zi-Xuan],
Ni, Y.[Yun],
Nai, L.F.[Li-Feng],
Wu, G.[Gang],
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],
Tian, Q.[Qi],
Adapting Shortcut with Normalizing Flow: An Efficient Tuning
Framework for Visual Recognition,
CVPR23(15965-15974)
IEEE DOI
2309
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Ni, M.H.[Min-Heng],
Huang, H.Y.[Hao-Yang],
Su, L.[Lin],
Cui, E.[Edward],
Bharti, T.[Taroon],
Wang, L.J.[Li-Juan],
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],
Tang, H.[Hao],
Kong, D.Y.[De-Ying],
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,
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Representation learning, Visualization, Image segmentation,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Domain Adaptation .