You, C.[Chong],
Li, C.[Chi],
Robinson, D.P.[Daniel P.],
Vidal, R.[René],
Self-Representation Based Unsupervised Exemplar Selection in a Union
of Subspaces,
PAMI(44), No. 5, May 2022, pp. 2698-2711.
IEEE DOI
2204
Clustering algorithms, Data models, Databases, Clustering methods,
Optimization, Image reconstruction,
subspace clustering
BibRef
Shi, W.J.[Wen-Jie],
Huang, G.[Gao],
Song, S.[Shiji],
Wang, Z.Y.[Zhuo-Yuan],
Lin, T.Y.[Ting-Yu],
Wu, C.[Cheng],
Self-Supervised Discovering of Interpretable Features for
Reinforcement Learning,
PAMI(44), No. 5, May 2022, pp. 2712-2724.
IEEE DOI
2204
Task analysis, Decision making, Perturbation methods,
Reinforcement learning, Jacobian matrices, Visualization, Games,
decision-making process
BibRef
Shi, W.J.[Wen-Jie],
Huang, G.[Gao],
Song, S.[Shiji],
Wu, C.[Cheng],
Temporal-Spatial Causal Interpretations for Vision-Based
Reinforcement Learning,
PAMI(44), No. 12, December 2022, pp. 10222-10235.
IEEE DOI
2212
Adaptation models, Reliability, Decision making,
Perturbation methods, Visualization, Task analysis,
temporal causality
BibRef
Durrant, A.[Aiden],
Leontidis, G.[Georgios],
Hyperspherically regularized networks for self-supervision,
IVC(124), 2022, pp. 104494.
Elsevier DOI
2208
Self-supervised learning, Representation learning,
Representation separability, Image classification
BibRef
Berg, P.[Paul],
Pham, M.T.[Minh-Tan],
Courty, N.[Nicolas],
Self-Supervised Learning for Scene Classification in Remote Sensing:
Current State of the Art and Perspectives,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Gao, Y.[Yuan],
Sun, X.J.[Xiao-Juan],
Liu, C.[Chao],
A General Self-Supervised Framework for Remote Sensing Image
Classification,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link
2210
BibRef
Chen, X.L.[Xi-Liang],
Zhu, G.B.[Guo-Bin],
Liu, M.Q.[Ming-Qing],
Remote Sensing Image Scene Classification with Self-Supervised
Learning Based on Partially Unlabeled Datasets,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Calhoun, Z.D.[Zachary D.],
Lahrichi, S.[Saad],
Ren, S.[Simiao],
Malof, J.M.[Jordan M.],
Bradbury, K.[Kyle],
Self-Supervised Encoders Are Better Transfer Learners in Remote
Sensing Applications,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Li, S.[Shuo],
Liu, F.[Fang],
Hao, Z.[Zehua],
Jiao, L.C.[Li-Cheng],
Liu, X.[Xu],
Guo, Y.W.[Yu-Wei],
MinEnt: Minimum entropy for self-supervised representation learning,
PR(138), 2023, pp. 109364.
Elsevier DOI
2303
Self-supervised learning, Minimum entropy,
Unsupervised representation learning, Image classification
BibRef
Wang, H.[Hanxuan],
Lu, N.[Na],
Luo, H.[Huan],
Liu, Q.[Qinyang],
Self-supervised clustering with assistance from off-the-shelf
classifier,
PR(138), 2023, pp. 109350.
Elsevier DOI
2303
Deep clustering, Classification, Self-supervised, Sample selection
BibRef
Cheng, H.Y.[Hao-Yang],
Li, H.L.[Hong-Liang],
Qiu, H.Q.[He-Qian],
Wu, Q.[Qingbo],
Zhang, X.L.[Xiao-Liang],
Meng, F.[Fanman],
Ngan, K.N.[King Ngi],
Unsupervised Visual Representation Learning via Multi-Dimensional
Relationship Alignment,
IP(32), 2023, pp. 1613-1626.
IEEE DOI
2303
Task analysis, Self-supervised learning, Image reconstruction, Optimization,
Visualization, Training, Representation learning, convolutional neural network
BibRef
Alfaro-Contreras, M.[María],
Ríos-Vila, A.[Antonio],
Valero-Mas, J.J.[Jose J.],
Calvo-Zaragoza, J.[Jorge],
Few-shot symbol classification via self-supervised learning and
nearest neighbor,
PRL(167), 2023, pp. 1-8.
Elsevier DOI
2303
Symbol classification, Document image analysis,
Self-Supervised learning, Few-Shot classification
BibRef
Qin, Y.[Yao],
Ye, Y.X.[Yuan-Xin],
Zhao, Y.[Yue],
Wu, J.Z.[Jun-Zheng],
Zhang, H.[Han],
Cheng, K.[Kenan],
Li, K.[Kun],
Nearest Neighboring Self-Supervised Learning for Hyperspectral Image
Classification,
RS(15), No. 6, 2023, pp. 1713.
DOI Link
2304
BibRef
Ye, F.[Fei],
Bors, A.G.[Adrian G.],
Dynamic Self-Supervised Teacher-Student Network Learning,
PAMI(45), No. 5, May 2023, pp. 5731-5748.
IEEE DOI
2304
Task analysis, Mixture models, Training,
Generative adversarial networks, Data models,
teacher-student framework
BibRef
Wei, L.H.[Long-Hui],
Xie, L.X.[Ling-Xi],
Zhou, W.G.[Wen-Gang],
Li, H.Q.[Hou-Qiang],
Tian, Q.[Qi],
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
BibRef
Wang, J.[Jiatai],
Xu, Z.W.[Zhi-Wei],
Yang, X.[Xuewen],
Guo, D.J.[Dong-Jin],
Liu, L.M.[Li-Min],
Self-supervised image clustering from multiple incomplete views via
constrastive complementary generation,
IET-CV(17), No. 2, 2023, pp. 189-202.
DOI Link
2304
clustering from multiple incomplete views, computer vision,
constrastive learning, generative adversarial network
BibRef
Wang, F.[Feng],
Kong, T.[Tao],
Zhang, R.[Rufeng],
Liu, H.P.[Hua-Ping],
Li, H.[Hang],
Self-Supervised Learning by Estimating Twin Class Distribution,
IP(32), 2023, pp. 2228-2236.
IEEE DOI
2305
Task analysis, Mutual information, Entropy,
Probability distribution, Self-supervised learning, image classification
BibRef
Zhao, W.[Wenyi],
Li, C.Y.[Chong-Yi],
Zhang, W.D.[Wei-Dong],
Yang, L.[Lu],
Zhuang, P.X.[Pei-Xian],
Li, L.Q.[Ling-Qiao],
Fan, K.[Kefeng],
Yang, H.H.[Hui-Hua],
Embedding Global Contrastive and Local Location in Self-Supervised
Learning,
CirSysVideo(33), No. 5, May 2023, pp. 2275-2289.
IEEE DOI
2305
Task analysis, Optimization, Feature extraction, Semantics, Training,
Ensemble learning, Data models,
ensemble learning
BibRef
Ge, J.[Jidong],
Liu, Y.X.[Yu-Xiang],
Gui, J.[Jie],
Fang, L.T.[Lan-Ting],
Lin, M.[Ming],
Kwok, J.T.Y.[James Tin-Yau],
Huang, L.G.[Li-Guo],
Luo, B.[Bin],
Learning the Relation Between Similarity Loss and Clustering Loss in
Self-Supervised Learning,
IP(32), 2023, pp. 3442-3454.
IEEE DOI
2307
Self-supervised learning, Training, Supervised learning,
Measurement, Task analysis, Probability distribution, image classification
BibRef
Peng, Z.H.[Zhi-Hao],
Liu, H.[Hui],
Jia, Y.H.[Yu-Heng],
Hou, J.H.[Jun-Hui],
Deep Attention-Guided Graph Clustering With Dual Self-Supervision,
CirSysVideo(33), No. 7, July 2023, pp. 3296-3307.
IEEE DOI
2307
Feature extraction, Decoding, Probability distribution,
Clustering methods, Decision making, Data mining, Limiting,
self-supervision
BibRef
Lialin, V.[Vladislav],
Rawls, S.[Stephen],
Chan, D.[David],
Ghosh, S.[Shalini],
Rumshisky, A.[Anna],
Hamza, W.[Wael],
Scalable and Accurate Self-supervised Multimodal Representation
Learning without Aligned Video and Text Data,
Pretrain23(390-400)
IEEE DOI
2302
Training, Representation learning, Adaptation models,
Visualization, Neural networks, Optimized production technology
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.[Chenyu],
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
Sinha, S.[Samarth],
Gehler, P.[Peter],
Locatello, F.[Francesco],
Schiele, B.[Bernt],
TeST: Test-time Self-Training under Distribution Shift,
WACV23(2758-2768)
IEEE DOI
2302
Training, Adaptation models, Image segmentation, Neural networks,
Object detection, Predictive models, Prediction algorithms, visual reasoning
BibRef
Mohamadi, S.[Salman],
Doretto, G.[Gianfranco],
Adjeroh, D.A.[Donald A.],
FUSSL: Fuzzy Uncertain Self Supervised Learning,
WACV23(2798-2807)
IEEE DOI
2302
Training, Representation learning, Protocols, Uncertainty,
Annotations, Redundancy, Self-supervised learning, visual reasoning
BibRef
Koçyigit, M.T.[Mustafa Taha],
Hospedales, T.M.[Timothy M.],
Bilen, H.[Hakan],
Accelerating Self-Supervised Learning via Efficient Training
Strategies,
WACV23(5643-5653)
IEEE DOI
2302
Training, Schedules, Visualization, Costs, Self-supervised learning,
Transformers, Algorithms: Machine learning architectures,
visual reasoning
BibRef
Chen, Z.[Zekai],
Agarwal, D.[Devansh],
Aggarwal, K.[Kshitij],
Safta, W.[Wiem],
Balan, M.M.[Mariann Micsinai],
Brown, K.[Kevin],
Masked Image Modeling Advances 3D Medical Image Analysis,
WACV23(1969-1979)
IEEE DOI
2302
WWW Link. Training, Solid modeling, Analytical models, Image segmentation,
Self-supervised learning, Predictive models.
BibRef
Yang, H.Y.[Hua-Yi],
Wang, D.Q.[De-Qing],
Zhao, Z.Y.[Zheng-Yang],
Wang, X.[Xuying],
SSL-DC: Improving Transduxctive Few-Shot Learning via Self-Supervised
Learning and Distribution Calibration,
ICPR22(4892-4898)
IEEE DOI
2212
Training, Art, Prototypes, Self-supervised learning,
Gaussian distribution, Generative adversarial networks, Calibration
BibRef
Huang, Y.H.[Yue-Hong],
Tseng, Y.C.[Yu-Chee],
A Self-Supervised Solution for the Switch-Toggling Visual Task,
ICPR22(3429-3435)
IEEE DOI
2212
Training, Visualization, Switches, Self-supervised learning,
Reinforcement learning, Cognition
BibRef
Liu, H.T.[Hao-Tian],
Cai, M.[Mu],
Lee, Y.J.[Yong Jae],
Masked Discrimination for Self-supervised Learning on Point Clouds,
ECCV22(II:657-675).
Springer DOI
2211
BibRef
Thoker, F.M.[Fida Mohammad],
Doughty, H.[Hazel],
Bagad, P.[Piyush],
Snoek, C.G.M.[Cees G.M.],
How Severe Is Benchmark-Sensitivity in Video Self-Supervised Learning?,
ECCV22(XXXIV:632-652).
Springer DOI
2211
WWW Link. Analyze self-supervised learning.
BibRef
Prabhu, S.M.[Sahana M.],
Katta, J.Y.[Jitendra Y.],
Kale, A.A.[Amit A.],
Self-Supervised Learning for Texture Classification Using Limited
Labeled Data,
ICIP22(1416-1420)
IEEE DOI
2211
Representation learning, Visualization, Training data,
Self-supervised learning, Performance gain, Data models
BibRef
Amrani, E.[Elad],
Karlinsky, L.[Leonid],
Bronstein, A.[Alex],
Self-Supervised Classification Network,
ECCV22(XXXI:116-132).
Springer DOI
2211
BibRef
Moon, W.J.[Won-Jun],
Kim, J.H.[Ji-Hwan],
Heo, J.P.[Jae-Pil],
Tailoring Self-Supervision for Supervised Learning,
ECCV22(XXV:346-364).
Springer DOI
2211
BibRef
Mu, N.[Norman],
Kirillov, A.[Alexander],
Wagner, D.[David],
Xie, S.[Saining],
SLIP: Self-supervision Meets Language-Image Pre-training,
ECCV22(XXVI:529-544).
Springer DOI
2211
BibRef
Quan, Y.H.[Yu-Hui],
Qin, X.R.[Xin-Ran],
Pang, T.Y.[Tong-Yao],
Ji, H.[Hui],
Dual-Domain Self-supervised Learning and Model Adaption for Deep
Compressive Imaging,
ECCV22(XXX:409-426).
Springer DOI
2211
BibRef
Peri, R.[Raghuveer],
Parthasarathy, S.[Srinivas],
Sundaram, S.[Shiva],
Scene Representation Learning from Videos Using Self-Supervised and
Weakly-Supervised Techniques,
ICIP22(1671-1675)
IEEE DOI
2211
Training, Representation learning, Image analysis,
Image recognition, Filtering, Image representation, self-supervised
BibRef
Addepalli, S.[Sravanti],
Bhogale, K.[Kaushal],
Dey, P.[Priyam],
Babu, R.V.[R. Venkatesh],
Towards Efficient and Effective Self-supervised Learning of Visual
Representations,
ECCV22(XXXI:523-538).
Springer DOI
2211
BibRef
Akiva, P.[Peri],
Purri, M.[Matthew],
Leotta, M.[Matthew],
Self-Supervised Material and Texture Representation Learning for
Remote Sensing Tasks,
CVPR22(8193-8205)
IEEE DOI
2210
Representation learning, Satellites, Semantics, Transfer learning,
Self-supervised learning, Surface texture, Task analysis,
Photogrammetry and remote sensing
BibRef
Du, Y.L.[Yi-Lun],
Gan, C.[Chuang],
Isola, P.[Phillip],
Curious Representation Learning for Embodied Intelligence,
ICCV21(10388-10397)
IEEE DOI
2203
Representation learning, Visualization, Navigation, Semantics,
Reinforcement learning, Internet, Representation learning,
BibRef
Zhou, H.Y.[Hong-Yu],
Lu, C.X.[Chi-Xiang],
Yang, S.[Sibei],
Han, X.G.[Xiao-Guang],
Yu, Y.Z.[Yi-Zhou],
Preservational Learning Improves Self-supervised Medical Image Models
by Reconstructing Diverse Contexts,
ICCV21(3479-3489)
IEEE DOI
2203
WWW Link. Representation learning, Protocols, Codes, Computational modeling,
Estimation, Task analysis, Medical, biological, and cell microscopy,
BibRef
Huang, S.Y.[Si-Yuan],
Xie, Y.C.[Yi-Chen],
Zhu, S.C.[Song-Chun],
Zhu, Y.X.[Yi-Xin],
Spatio-temporal Self-Supervised Representation Learning for 3D Point
Clouds,
ICCV21(6515-6525)
IEEE DOI
2203
Point cloud compression, Representation learning, Training,
Solid modeling, Visualization, Supervised learning, Stereo,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Hu, K.[Kai],
Shao, J.[Jie],
Liu, Y.[Yuan],
Raj, B.[Bhiksha],
Savvides, M.[Marios],
Shen, Z.Q.[Zhi-Qiang],
Contrast and Order Representations for Video Self-Supervised Learning,
ICCV21(7919-7929)
IEEE DOI
2203
Representation learning, Computational modeling,
Predictive models, Task analysis, Videos, Representation learning
BibRef
Qian, R.[Rui],
Ding, S.R.[Shuang-Rui],
Liu, X.[Xian],
Lin, D.[Dahua],
Static and Dynamic Concepts for Self-Supervised Video Representation
Learning,
ECCV22(XXVI:145-164).
Springer DOI
2211
BibRef
Qian, R.[Rui],
Li, Y.X.[Yu-Xi],
Liu, H.B.[Hua-Bin],
See, J.[John],
Ding, S.R.[Shuang-Rui],
Liu, X.[Xian],
Li, D.[Dian],
Lin, W.Y.[Wei-Yao],
Enhancing Self-supervised Video Representation Learning via
Multi-level Feature Optimization,
ICCV21(7970-7981)
IEEE DOI
2203
Representation learning, Codes, Computational modeling, Semantics,
Reliability, Task analysis, Video analysis and understanding,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Huang, D.[Deng],
Wu, W.H.[Wen-Hao],
Hu, W.[Weiwen],
Liu, X.[Xu],
He, D.L.[Dong-Liang],
Wu, Z.H.[Zhi-Hua],
Wu, X.M.[Xiang-Miao],
Tan, M.K.[Ming-Kui],
Ding, E.[Errui],
ASCNet: Self-Supervised Video Representation Learning with
Appearance-Speed Consistency,
ICCV21(8076-8085)
IEEE DOI
2203
Representation learning, Visualization, Image recognition, Codes,
Noise measurement, Data mining, Video analysis and understanding,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Kim, D.H.[Dong-Hyun],
Saito, K.[Kuniaki],
Oh, T.H.[Tae-Hyun],
Plummer, B.A.[Bryan A.],
Sclaroff, S.[Stan],
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
BibRef
Wang, G.[Guangrun],
Wang, K.[Keze],
Wang, G.[Guangcong],
Torr, P.H.S.[Philip H.S.],
Lin, L.[Liang],
Solving Inefficiency of Self-supervised Representation Learning,
ICCV21(9485-9495)
IEEE DOI
2203
Training, Representation learning, Computational modeling,
Supervised learning, Benchmark testing, Task analysis,
Representation learning
BibRef
Patrick, M.[Mandela],
Asano, Y.M.[Yuki M.],
Kuznetsova, P.[Polina],
Fong, R.[Ruth],
Henriques, J.F.[João F.],
Zweig, G.[Geoffrey],
Vedaldi, A.[Andrea],
On Compositions of Transformations in Contrastive Self-Supervised
Learning,
ICCV21(9557-9567)
IEEE DOI
2203
Codes, Benchmark testing, Encoding, Standards, Videos,
Representation learning, Vision + other modalities
BibRef
Hua, T.Y.[Tian-Yu],
Wang, W.X.[Wen-Xiao],
Xue, Z.[Zihui],
Ren, S.[Sucheng],
Wang, Y.[Yue],
Zhao, H.[Hang],
On Feature Decorrelation in Self-Supervised Learning,
ICCV21(9578-9588)
IEEE DOI
2203
Representation learning, Correlation, Robustness, Decorrelation,
Covariance matrices, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Kotar, K.[Klemen],
Ilharco, G.[Gabriel],
Schmidt, L.[Ludwig],
Ehsani, K.[Kiana],
Mottaghi, R.[Roozbeh],
Contrasting Contrastive Self-Supervised Representation Learning
Pipelines,
ICCV21(9929-9939)
IEEE DOI
2203
Representation learning, Training, Visualization, Pipelines,
Benchmark testing, Data models, Representation learning,
BibRef
Tian, Y.L.[Yong-Long],
Hénaff, O.J.[Olivier J.],
van den Oord, A.[Aäron],
Divide and Contrast: Self-supervised Learning from Uncurated Data,
ICCV21(10043-10054)
IEEE DOI
2203
Annotations, Benchmark testing, Data mining, Task analysis,
Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhao, Y.C.[Yu-Cheng],
Wang, G.[Guangting],
Luo, C.[Chong],
Zeng, W.J.[Wen-Jun],
Zha, Z.J.[Zheng-Jun],
Self-Supervised Visual Representations Learning by Contrastive Mask
Prediction,
ICCV21(10140-10149)
IEEE DOI
2203
Training, Representation learning, Visualization, Head, Semantics,
Performance gain, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Koohpayegani, S.A.[Soroush Abbasi],
Tejankar, A.[Ajinkya],
Pirsiavash, H.[Hamed],
Mean Shift for Self-Supervised Learning,
ICCV21(10306-10315)
IEEE DOI
2203
Codes, Clustering algorithms, Task analysis,
Residual neural networks, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Gavrilyuk, K.[Kirill],
Jain, M.[Mihir],
Karmanov, I.[Ilia],
Snoek, C.G.M.[Cees G. M.],
Motion-Augmented Self-Training for Video Recognition at Smaller Scale,
ICCV21(10409-10418)
IEEE DOI
2203
Training, Optical losses, Knowledge engineering,
Computational modeling, Semisupervised learning,
Action and behavior recognition
BibRef
Pantazis, O.[Omiros],
Brostow, G.J.[Gabriel J.],
Jones, K.E.[Kate E.],
Aodha, O.M.[Oisin Mac],
Focus on the Positives:
Self-Supervised Learning for Biodiversity Monitoring,
ICCV21(10563-10572)
IEEE DOI
2203
Training, Visualization, Transfer learning, Benchmark testing,
Cameras, Biodiversity, Representation learning, Medical, biological,
Recognition and classification
BibRef
Zhang, L.Z.[Ling-Zhi],
Du, W.Y.[Wei-Yu],
Zhou, S.H.[Sheng-Hao],
Wang, J.C.[Jian-Cong],
Shi, J.B.[Jian-Bo],
Inpaint2Learn: A Self-Supervised Framework for Affordance Learning,
WACV22(3778-3787)
IEEE DOI
2202
Training, Affordances, Pipelines, Predictive models,
Benchmark testing, Adversarial machine learning,
Analysis and Understanding Scene Understanding
BibRef
Mazumder, P.[Pratik],
Singh, P.[Pravendra],
Namboodiri, V.P.[Vinay P.],
Fair Visual Recognition in Limited Data Regime using Self-Supervision
and Self-Distillation,
WACV22(3889-3897)
IEEE DOI
2202
Training, Deep learning, Adaptation models, Visualization,
Computational modeling, Training data,
Privacy and Ethics in Vision
BibRef
Zheltonozhskii, E.[Evgenii],
Baskin, C.[Chaim],
Mendelson, A.[Avi],
Bronstein, A.M.[Alex M.],
Litany, O.[Or],
Contrast to Divide: Self-Supervised Pre-Training for Learning with
Noisy Labels,
WACV22(387-397)
IEEE DOI
2202
Training, Upper bound, Neural networks,
Semisupervised learning, Feature extraction, Robustness, Transfer,
Large-scale Vision Applications
BibRef
Reed, C.J.[Colorado J.],
Yue, X.Y.[Xiang-Yu],
Nrusimha, A.[Ani],
Ebrahimi, S.[Sayna],
Vijaykumar, V.[Vivek],
Mao, R.[Richard],
Li, B.[Bo],
Zhang, S.H.[Shang-Hang],
Guillory, D.[Devin],
Metzger, S.[Sean],
Keutzer, K.[Kurt],
Darrell, T.J.[Trevor J.],
Self-Supervised Pretraining Improves Self-Supervised Pretraining,
WACV22(1050-1060)
IEEE DOI
2202
Computational modeling, Data models, Robustness,
Task analysis, X-ray imaging, Convergence, Transfer, Few-shot,
Vision for Aerial/Drone/Underwater/Ground Vehicles
BibRef
Zhang, Z.[Zehua],
Crandall, D.[David],
Hierarchically Decoupled Spatial-Temporal Contrast for
Self-supervised Video Representation Learning,
WACV22(975-985)
IEEE DOI
2202
Representation learning, Deep learning, Codes,
Semantics, Supervised learning, Benchmark testing, Transfer,
Analysis and Understanding
BibRef
Huynh, T.[Tri],
Kornblith, S.[Simon],
Walter, M.R.[Matthew R.],
Maire, M.[Michael],
Khademi, M.[Maryam],
Boosting Contrastive Self-Supervised Learning with False Negative
Cancellation,
WACV22(986-996)
IEEE DOI
2202
Representation learning, Visualization, Codes,
Computational modeling, Semantics, Boosting, Transfer, Few-shot,
Semi- and Un- supervised Learning Deep Learning
BibRef
Yamaguchi, S.[Shin'ya],
Kanai, S.[Sekitoshi],
Shioda, T.[Tetsuya],
Takeda, S.[Shoichiro],
Image Enhanced Rotation Prediction for Self-Supervised Learning,
ICIP21(489-493)
IEEE DOI
2201
Shape, Predictive models, Network architecture, Benchmark testing,
Task analysis, Image enhancement, Self-supervised learning, CNN
BibRef
Selvaraju, R.R.[Ramprasaath R.],
Desai, K.[Karan],
Johnson, J.[Justin],
Naik, N.[Nikhil],
CASTing Your Model:
Learning to Localize Improves Self-Supervised Representations,
CVPR21(11053-11062)
IEEE DOI
2111
Visualization, Correlation, Codes, Grounding, Crops, Robustness
BibRef
Gudovskiy, D.,
Hodgkinson, A.,
Yamaguchi, T.,
Tsukizawa, S.,
Deep Active Learning for Biased Datasets via Fisher Kernel
Self-Supervision,
CVPR20(9038-9046)
IEEE DOI
2008
Task analysis, Training, Kernel, Labeling, Artificial intelligence,
Data models, Training data
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Self-Supervised Learning for Object Detection and Segmentation .