Hu, X.[Xiang],
Li, T.[Teng],
Zhou, T.[Tong],
Peng, Y.X.[Yuan-Xi],
Deep Spatial-Spectral Subspace Clustering for Hyperspectral Images
Based on Contrastive Learning,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Hassanin, M.[Mohammed],
Radwan, I.[Ibrahim],
Khan, S.[Salman],
Tahtali, M.[Murat],
Learning discriminative representations for multi-label image
recognition,
JVCIR(83), 2022, pp. 103448.
Elsevier DOI
2202
Multi-label recognition, Multi-label-contrastive learning,
Contrastive representation, Deep learning
BibRef
Dave, I.[Ishan],
Gupta, R.[Rohit],
Rizve, M.N.[Mamshad Nayeem],
Shah, M.[Mubarak],
TCLR: Temporal contrastive learning for video representation,
CVIU(219), 2022, pp. 103406.
Elsevier DOI
2205
Self-Supervised Learning, Action Recognition, Video Representation
BibRef
Li, Y.F.[Yun-Fan],
Yang, M.X.[Mou-Xing],
Peng, D.Z.[De-Zhong],
Li, T.H.[Tai-Hao],
Huang, J.T.[Jian-Tao],
Peng, X.[Xi],
Twin Contrastive Learning for Online Clustering,
IJCV(130), No. 9, September 2022, pp. 2205-2221.
Springer DOI
2208
BibRef
Zhu, H.[He],
Yu, S.[Shan],
Retaining Diverse Information in Contrastive Learning Through
Multiple Projectors,
SPLetters(29), 2022, pp. 1789-1793.
IEEE DOI
2209
Visualization, Training, Task analysis, Representation learning,
Feature extraction, Periodic structures, Indexes, projector mining
BibRef
Huo, X.Y.[Xin-Yue],
Xie, L.X.[Ling-Xi],
Wei, L.H.[Long-Hui],
Zhang, X.P.[Xiao-Peng],
Chen, X.[Xin],
Li, H.[Hao],
Yang, Z.[Zijie],
Zhou, W.G.[Wen-Gang],
Li, H.Q.[Hou-Qiang],
Tian, Q.[Qi],
Heterogeneous Contrastive Learning:
Encoding Spatial Information for Compact Visual Representations,
MultMed(24), 2022, pp. 4224-4235.
IEEE DOI
2209
Pretraining -- improve efficiency.
Feature extraction, Semantics, Head, Contrastive learning,
pre-training, spatial information
BibRef
Liu, C.F.[Chen-Fang],
Sun, H.[Hao],
Xu, Y.[Yanjie],
Kuang, G.Y.[Gang-Yao],
Multi-Source Remote Sensing Pretraining Based on Contrastive
Self-Supervised Learning,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Xu, H.H.[Hao-Hang],
Xiong, H.K.[Hong-Kai],
Qi, G.J.[Guo-Jun],
K-Shot Contrastive Learning of Visual Features With Multiple Instance
Augmentations,
PAMI(44), No. 11, November 2022, pp. 8694-8700.
IEEE DOI
2210
Task analysis, Visualization, Training,
Eigenvalues and eigenfunctions, Dictionaries, contrastive learning
BibRef
Zhu, Y.S.[Yi-Sheng],
Shuai, H.[Hui],
Liu, G.C.[Guang-Can],
Liu, Q.S.[Qing-Shan],
Self-Supervised Video Representation Learning Using Improved
Instance-Wise Contrastive Learning and Deep Clustering,
CirSysVideo(32), No. 10, October 2022, pp. 6741-6752.
IEEE DOI
2210
Task analysis, Representation learning, Visualization,
Integrated circuit modeling, Training, Spatiotemporal phenomena,
deep clustering
BibRef
Peng, R.[Rui],
Zhao, W.Z.[Wen-Zhi],
Li, K.[Kaiyuan],
Ji, F.C.[Feng-Cheng],
Rong, C.X.[Cai-Xia],
Continual Contrastive Learning for Cross-Dataset Scene Classification,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Chen, Z.[Zihan],
Zhu, H.Y.[Hong-Yuan],
Cheng, H.[Hao],
Mi, S.[Siya],
Zhang, Y.[Yu],
Geng, X.[Xin],
LPCL: Localized prominence contrastive learning for self-supervised
dense visual pre-training,
PR(135), 2023, pp. 109185.
Elsevier DOI
2212
Self-supervised learning, Contrastive learning, Dense representation
BibRef
Hu, Z.[Ziye],
Li, W.[Wei],
Gan, Z.X.[Zhong-Xue],
Guo, W.[Weikun],
Zhu, J.[Jiwei],
Wen, J.Z.Q.[James Zhi-Qing],
Zhou, D.[Decheng],
Learning From Visual Demonstrations via Replayed Task-Contrastive
Model-Agnostic Meta-Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8756-8767.
IEEE DOI
2212
Robots, Microstrip, Visualization, Adaptation models, Training data,
Reinforcement learning, Meta-learning,
learning to learn
BibRef
Lee, H.[Hyunsub],
Choi, H.[Heeyoul],
Partitioning Image Representation in Contrastive Learning,
ICPR22(2864-2870)
IEEE DOI
2212
Image representation, Task analysis
BibRef
Lee, J.[Joonseok],
Joe, S.[Seongho],
Park, K.[Kyoungwon],
Kim, B.[Bogun],
Kang, H.[Hoyoung],
Park, J.[Jaeseon],
Gwon, Y.[Youngjune],
Shuffle and Divide: Contrastive Learning for Long Text,
ICPR22(2935-2941)
IEEE DOI
2212
Couplings, Text categorization, Clustering algorithms,
Self-supervised learning, Transformers, Classification algorithms
BibRef
Joe, S.[Seongho],
Kim, B.[Byoungjip],
Kang, H.[Hoyoung],
Park, K.[Kyoungwon],
Kim, B.[Bogun],
Park, J.[Jaeseon],
Lee, J.[Joonseok],
Gwon, Y.[Youngjune],
ContraCluster: Learning to Classify without Labels by Contrastive
Self-Supervision and Prototype-Based Semi-Supervision,
ICPR22(4685-4692)
IEEE DOI
2212
Representation learning, Pipelines, Prototypes,
Self-supervised learning, Benchmark testing, Noise measurement
BibRef
Jain, A.[Anurag],
Verma, Y.[Yashaswi],
Cross-modal Retrieval Using Contrastive Learning of Visual-Semantic
Embeddings,
ICPR22(4693-4699)
IEEE DOI
2212
Training, Adaptation models, Codes, Reproducibility of results,
Task analysis, Pattern matching, Image classification
BibRef
Wei, J.T.[Jiu-Tong],
Luo, G.[Guan],
Li, B.[Bing],
Hu, W.M.[Wei-Ming],
Inter-Intra Cross-Modality Self-Supervised Video Representation
Learning by Contrastive Clustering,
ICPR22(4815-4821)
IEEE DOI
2212
Representation learning, Visualization, Correlation, Semantics,
Self-supervised learning, Encoding
BibRef
Song, D.M.[Dan-Ming],
Gao, Y.P.[Yi-Peng],
Yan, J.K.[Jun-Kai],
Sun, W.[Wei],
Zheng, W.S.[Wei-Shi],
Space-correlated Contrastive Representation Learning with Multiple
Instances,
ICPR22(4715-4721)
IEEE DOI
2212
Learning systems, Representation learning, Image segmentation,
Semantics, Object detection, Data mining, Task analysis
BibRef
Yang, Z.Y.[Zheng-Yuan],
Liu, J.G.[Jin-Gen],
Huang, J.[Jing],
He, X.D.[Xiao-Dong],
Mei, T.[Tao],
Xu, C.L.[Chen-Liang],
Luo, J.B.[Jie-Bo],
Cross-modal Contrastive Distillation for Instructional Activity
Anticipation,
ICPR22(5002-5009)
IEEE DOI
2212
Charge coupled devices, Visualization, Semantics,
Natural languages, Benchmark testing, Data mining, Task analysis
BibRef
Lin, Z.[Ziyi],
Geng, S.J.[Shi-Jie],
Zhang, R.[Renrui],
Gao, P.[Peng],
de Melo, G.[Gerard],
Wang, X.G.[Xiao-Gang],
Dai, J.[Jifeng],
Qiao, Y.[Yu],
Li, H.S.[Hong-Sheng],
Frozen CLIP Models are Efficient Video Learners,
ECCV22(XXXV:388-404).
Springer DOI
2211
BibRef
Wang, H.Q.[Hao-Qing],
Deng, Z.H.[Zhi-Hong],
Contrastive Prototypical Network with Wasserstein Confidence Penalty,
ECCV22(XIX:665-682).
Springer DOI
2211
BibRef
Müller, P.[Philip],
Kaissis, G.[Georgios],
Zou, C.[Congyu],
Rueckert, D.[Daniel],
Joint Learning of Localized Representations from Medical Images and
Reports,
ECCV22(XXVI:685-701).
Springer DOI
2211
BibRef
Li, Z.Q.[Zi-Qiang],
Wang, C.Y.[Chao-Yue],
Zheng, H.L.[He-Liang],
Zhang, J.[Jing],
Li, B.[Bin],
FakeCLR: Exploring Contrastive Learning for Solving Latent
Discontinuity in Data-Efficient GANs,
ECCV22(XV:598-615).
Springer DOI
2211
WWW Link.
BibRef
Li, Y.H.[Yu-Heng],
Li, Y.J.[Yi-Jun],
Lu, J.W.[Jing-Wan],
Shechtman, E.[Eli],
Lee, Y.J.[Yong Jae],
Singh, K.K.[Krishna Kumar],
Contrastive Learning for Diverse Disentangled Foreground Generation,
ECCV22(XVI:334-351).
Springer DOI
2211
BibRef
Wang, X.[Xi],
Fu, X.[Xueyang],
Zhu, Y.[Yurui],
Zha, Z.J.[Zheng-Jun],
JPEG Artifacts Removal via Contrastive Representation Learning,
ECCV22(XVII:615-631).
Springer DOI
2211
BibRef
Reiß, S.[Simon],
Seibold, C.[Constantin],
Freytag, A.[Alexander],
Rodner, E.[Erik],
Stiefelhagen, R.[Rainer],
Graph-Constrained Contrastive Regularization for Semi-weakly Volumetric
Segmentation,
ECCV22(XXI:401-419).
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
Zhang, L.F.[Lin-Feng],
Chen, X.[Xin],
Zhang, J.[Junbo],
Dong, R.[Runpei],
Ma, K.[Kaisheng],
Contrastive Deep Supervision,
ECCV22(XXVI:1-19).
Springer DOI
2211
BibRef
Ci, Y.Z.[Yuan-Zheng],
Lin, C.[Chen],
Bai, L.[Lei],
Ouyang, W.L.[Wan-Li],
Fast-MoCo: Boost Momentum-Based Contrastive Learning with Combinatorial
Patches,
ECCV22(XXVI:290-306).
Springer DOI
2211
BibRef
Kahana, J.[Jonathan],
Hoshen, Y.[Yedid],
A Contrastive Objective for Learning Disentangled Representations,
ECCV22(XXVI:579-595).
Springer DOI
2211
BibRef
Tang, S.X.[Shi-Xiang],
Zhu, F.[Feng],
Bai, L.[Lei],
Zhao, R.[Rui],
Wang, C.[Chenyu],
Ouyang, W.L.[Wan-Li],
Unifying Visual Contrastive Learning for Object Recognition from a
Graph Perspective,
ECCV22(XXVI:649-667).
Springer DOI
2211
BibRef
Yeh, C.H.[Chun-Hsiao],
Hong, C.Y.[Cheng-Yao],
Hsu, Y.C.[Yen-Chi],
Liu, T.L.[Tyng-Luh],
Chen, Y.[Yubei],
LeCun, Y.[Yann],
Decoupled Contrastive Learning,
ECCV22(XXVI:668-684).
Springer DOI
2211
BibRef
Tang, S.X.[Shi-Xiang],
Zhu, F.[Feng],
Bai, L.[Lei],
Zhao, R.[Rui],
Ouyang, W.L.[Wan-Li],
Relative Contrastive Loss for Unsupervised Representation Learning,
ECCV22(XXVII:1-18).
Springer DOI
2211
BibRef
You, H.[Haoxuan],
Zhou, L.[Luowei],
Xiao, B.[Bin],
Codella, N.[Noel],
Cheng, Y.[Yu],
Xu, R.[Ruochen],
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
Moskalev, A.[Artem],
Sosnovik, I.[Ivan],
Fischer, V.[Volker],
Smeulders, A.[Arnold],
Contrasting Quadratic Assignments for Set-Based Representation Learning,
ECCV22(XXVII:88-104).
Springer DOI
2211
BibRef
Zhang, C.N.[Chao-Ning],
Zhang, K.[Kang],
Zhang, C.S.[Chen-Shuang],
Niu, A.[Axi],
Feng, J.[Jiu],
Yoo, C.D.[Chang D.],
Kweon, I.S.[In So],
Decoupled Adversarial Contrastive Learning for Self-supervised
Adversarial Robustness,
ECCV22(XXX:725-742).
Springer DOI
2211
BibRef
Xu, Y.[Yufei],
Zhang, Q.M.[Qi-Ming],
Zhang, J.[Jing],
Tao, D.C.[Da-Cheng],
RegionCL: Exploring Contrastive Region Pairs for Self-supervised
Representation Learning,
ECCV22(XXXIII:477-494).
Springer DOI
2211
BibRef
Xiao, F.[Fanyi],
Tighe, J.[Joseph],
Modolo, D.[Davide],
MaCLR: Motion-Aware Contrastive Learning of Representations for Videos,
ECCV22(XXXV:353-370).
Springer DOI
2211
BibRef
Ni, J.C.[Jing-Cheng],
Zhou, N.[Nan],
Qin, J.[Jie],
Wu, Q.[Qian],
Liu, J.Q.[Jun-Qi],
Li, B.X.[Bo-Xun],
Huang, D.[Di],
Motion Sensitive Contrastive Learning for Self-Supervised Video
Representation,
ECCV22(XXXV:457-474).
Springer DOI
2211
BibRef
Wang, H.R.[Hao-Ran],
He, D.L.[Dong-Liang],
Wu, W.H.[Wen-Hao],
Xia, B.[Boyang],
Yang, M.[Min],
Li, F.[Fu],
Yu, Y.L.[Yun-Long],
Ji, Z.[Zhong],
Ding, E.[Errui],
Wang, J.D.[Jing-Dong],
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for
Image-Text Retrieval,
ECCV22(XXXVI:700-716).
Springer DOI
2211
BibRef
Jiang, B.[Bo],
Krim, H.[Hamid],
Wu, T.F.[Tian-Fu],
Cansever, D.[Derya],
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric,
ICIP22(76-80)
IEEE DOI
2211
Training, Manifolds, Correlation, Fuses, Refining, Imaging,
Self-Supervised learning, Contrastive Learning, Jaccard Index, Non-linearity
BibRef
Shang, Y.Z.[Yu-Zhang],
Xu, D.[Dan],
Zong, Z.L.[Zi-Liang],
Nie, L.Q.[Li-Qiang],
Yan, Y.[Yan],
Network Binarization via Contrastive Learning,
ECCV22(XI:586-602).
Springer DOI
2211
BibRef
Li, M.Z.[Ming-Zhe],
Zhang, H.B.[Hong-Bo],
Lei, Q.[Qing],
Fan, Z.W.[Zong-Wen],
Liu, J.H.[Jing-Hua],
Du, J.X.[Ji-Xiang],
Pairwise Contrastive Learning Network for Action Quality Assessment,
ECCV22(IV:457-473).
Springer DOI
2211
BibRef
Yu, Q.Y.[Qi-Ying],
Lou, J.[Jieming],
Zhan, X.Y.[Xian-Yuan],
Li, Q.Z.[Qi-Zhang],
Zuo, W.M.[Wang-Meng],
Liu, Y.[Yang],
Liu, J.J.[Jing-Jing],
Adversarial Contrastive Learning via Asymmetric InfoNCE,
ECCV22(V:53-69).
Springer DOI
2211
BibRef
Peng, X.Y.[Xiang-Yu],
Wang, K.[Kai],
Zhu, Z.[Zheng],
Wang, M.[Mang],
You, Y.[Yang],
Crafting Better Contrastive Views for Siamese Representation Learning,
CVPR22(16010-16019)
IEEE DOI
2210
Representation learning, Training, Location awareness,
Image segmentation, Semantics, Crops, Representation learning,
Self- semi- meta- unsupervised learning
BibRef
Rao, Y.M.[Yong-Ming],
Zhao, W.L.[Wen-Liang],
Chen, G.Y.[Guang-Yi],
Tang, Y.S.[Yan-Song],
Zhu, Z.[Zheng],
Huang, G.[Guan],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
DenseCLIP: Language-Guided Dense Prediction with Context-Aware
Prompting,
CVPR22(18061-18070)
IEEE DOI
2210
Representation learning, Image segmentation, Visualization, Shape,
Computational modeling, Semantics, Predictive models,
grouping and shape analysis
BibRef
Khandelwal, A.[Apoorv],
Weihs, L.[Luca],
Mottaghi, R.[Roozbeh],
Kembhavi, A.[Aniruddha],
Simple but Effective: CLIP Embeddings for Embodied AI,
CVPR22(14809-14818)
IEEE DOI
2210
Contrastive Language Image Pretraining.
Training, Measurement, Visualization, Navigation, Semantics,
Robot vision systems, Robot vision, Navigation and autonomous driving
BibRef
Ding, S.R.[Shuang-Rui],
Li, M.[Maomao],
Yang, T.[Tianyu],
Qian, R.[Rui],
Xu, H.H.[Hao-Hang],
Chen, Q.Y.[Qing-Yi],
Wang, J.[Jue],
Xiong, H.K.[Hong-Kai],
Motion-aware Contrastive Video Representation Learning via
Foreground-background Merging,
CVPR22(9706-9716)
IEEE DOI
2210
Representation learning, Image color analysis, Merging, Semantics,
Resists, Detectors, Self- semi- meta- Representation learning
BibRef
Guo, Y.F.[Yuan-Fan],
Xu, M.H.[Ming-Hao],
Li, J.[Jiawen],
Ni, B.B.[Bing-Bing],
Zhu, X.Y.[Xuan-Yu],
Sun, Z.B.[Zhen-Bang],
Xu, Y.[Yi],
HCSC: Hierarchical Contrastive Selective Coding,
CVPR22(9696-9705)
IEEE DOI
2210
WWW Link. Representation learning, Codes, Computational modeling, Semantics,
Prototypes, Image representation, Self- semi- meta- Representation learning
BibRef
Tan, C.[Cheng],
Gao, Z.Y.[Zhang-Yang],
Wu, L.R.[Li-Rong],
Li, S.Y.[Si-Yuan],
Li, S.Z.[Stan Z.],
Hyperspherical Consistency Regularization,
CVPR22(7234-7245)
IEEE DOI
2210
Deep learning, Training, Geometry, Supervised learning,
Self-supervised learning, Semisupervised learning,
Vision applications and systems
BibRef
Ko, D.[Dohwan],
Choi, J.[Joonmyung],
Ko, J.[Juyeon],
Noh, S.[Shinyeong],
On, K.W.[Kyoung-Woon],
Kim, E.S.[Eun-Sol],
Kim, H.W.J.[Hyun-Woo J.],
Video-Text Representation Learning via Differentiable Weak Temporal
Alignment,
CVPR22(5006-5015)
IEEE DOI
2210
Code, Contrastive Learning.
WWW Link. Representation learning, Codes, Computational modeling,
Self-supervised learning, Data models, Pattern recognition,
Self- semi- meta- Video analysis and understanding
BibRef
Dorkenwald, M.[Michael],
Xiao, F.[Fanyi],
Brattoli, B.[Biagio],
Tighe, J.[Joseph],
Modolo, D.[Davide],
SCVRL: Shuffled Contrastive Video Representation Learning,
L3D-IVU22(4131-4140)
IEEE DOI
2210
Representation learning, Visualization, Semantics,
Self-supervised learning, Benchmark testing, Transformers, Pattern recognition
BibRef
Chen, D.[Dian],
Wang, D.[Dequan],
Darrell, T.J.[Trevor J.],
Ebrahimi, S.[Sayna],
Contrastive Test-Time Adaptation,
CVPR22(295-305)
IEEE DOI
2210
Representation learning, Adaptation models, Memory management,
Benchmark testing, Data models, Calibration, Pattern recognition,
Self- semi- meta- unsupervised learning
BibRef
Taleb, A.[Aiham],
Kirchler, M.[Matthias],
Monti, R.[Remo],
Lippert, C.[Christoph],
ContIG: Self-supervised Multimodal Contrastive Learning for Medical
Imaging with Genetics,
CVPR22(20876-20889)
IEEE DOI
2210
Deep learning, Adaptation models, Costs, Design methodology,
Computational modeling, Genomics, Medical,
Vision applications and systems
BibRef
Dong, X.[Xiao],
Zhan, X.[Xunlin],
Wu, Y.X.[Yang-Xin],
Wei, Y.C.[Yun-Chao],
Kampffmeyer, M.C.[Michael C.],
Wei, X.Y.[Xiao-Yong],
Lu, M.[Minlong],
Wang, Y.[Yaowei],
Liang, X.D.[Xiao-Dan],
M5Product: Self-harmonized Contrastive Learning for E-commercial
Multi-modal Pretraining,
CVPR22(21220-21230)
IEEE DOI
2210
Representation learning, Adaptation models, Codes,
Computational modeling, Semantics, Transformers,
BibRef
Zhao, B.[Bing],
Li, J.[Jun],
Zhu, H.[Hong],
CoDo: Contrastive Learning with Downstream Background Invariance for
Detection,
L3D-IVU22(4195-4200)
IEEE DOI
2210
Representation learning, Visualization, Transfer learning,
Supervised learning, Pipelines, Object detection, Self-supervised learning
BibRef
Zhang, C.N.[Chao-Ning],
Zhang, K.[Kang],
Pham, T.X.[Trung X.],
Niu, A.[Axi],
Qiao, Z.[Zhinan],
Yoo, C.D.[Chang D.],
Kweon, I.S.[In So],
Dual Temperature Helps Contrastive Learning Without Many Negative
Samples: Towards Understanding and Simplifying MoCo,
CVPR22(14421-14430)
IEEE DOI
2210
Dictionaries, Temperature, Codes, Temperature control,
Pattern recognition, Queueing analysis, Self- semi- meta- unsupervised learning
BibRef
Son, J.[Jeany],
Contrastive Learning for Space-time Correspondence via Self-cycle
Consistency,
CVPR22(14659-14668)
IEEE DOI
2210
Training, Uncertainty, Government, Self-supervised learning,
Filtering algorithms, Probabilistic logic, Bayes methods,
grouping and shape analysis
BibRef
Cole, E.[Elijah],
Yang, X.[Xuan],
Wilber, K.[Kimberly],
Aodha, O.M.[Oisin Mac],
Belongie, S.[Serge],
When Does Contrastive Visual Representation Learning Work?,
CVPR22(01-10)
IEEE DOI
2210
Representation learning, Training, Visualization, Protocols,
Supervised learning, Self-supervised learning,
Transfer/low-shot/long-tail learning
BibRef
Wang, Z.T.[Zhen-Ting],
Zhai, J.[Juan],
Ma, S.Q.[Shi-Qing],
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural
Networks via Image Quantization and Contrastive Adversarial Learning,
CVPR22(15054-15063)
IEEE DOI
2210
Training, Deep learning, Quantization (signal), Codes,
Neural networks, Visual systems, Adversarial attack and defense
BibRef
Wang, H.Q.[Hao-Qing],
Guo, X.[Xun],
Deng, Z.[ZhiHong],
Lu, Y.[Yan],
Rethinking Minimal Sufficient Representation in Contrastive Learning,
CVPR22(16020-16029)
IEEE DOI
2210
Training, Representation learning, Degradation,
Self-supervised learning, Transformers, Pattern recognition,
Self- semi- meta- Representation learning
BibRef
Yi, L.[Li],
Liu, S.[Sheng],
She, Q.[Qi],
McLeod, A.I.[A. Ian],
Wang, B.[Boyu],
On Learning Contrastive Representations for Learning with Noisy
Labels,
CVPR22(16661-16670)
IEEE DOI
2210
Representation learning, Deep learning, Ethics, Neural networks,
Entropy, Noise robustness, Representation learning,
privacy and ethics in vision
BibRef
Ma, H.Y.[Hao-Yu],
Zhao, H.[Handong],
Lin, Z.[Zhe],
Kale, A.[Ajinkya],
Wang, Z.Y.[Zhang-Yang],
Yu, T.[Tong],
Gu, J.[Jiuxiang],
Choudhary, S.[Sunav],
Xie, X.H.[Xiao-Hui],
EI-CLIP: Entity-aware Interventional Contrastive Learning for
E-commerce Cross-modal Retrieval,
CVPR22(18030-18040)
IEEE DOI
2210
Design methodology, Semantics, Clothing, Metadata, Benchmark testing,
Search problems,
BibRef
Chen, C.[Cheng],
Tan, Z.[Zhenshan],
Cheng, Q.[Qingrong],
Jiang, X.[Xin],
Liu, Q.[Qun],
Zhu, Y.D.[Yu-Dong],
Gu, X.D.[Xiao-Dong],
UTC: A Unified Transformer with Inter-Task Contrastive Learning for
Visual Dialog,
CVPR22(18082-18091)
IEEE DOI
2210
Training, Representation learning, Visualization, Correlation,
Transformers, Pattern recognition, Machine learning
BibRef
Yang, J.W.[Jian-Wei],
Li, C.Y.[Chun-Yuan],
Zhang, P.[Pengchuan],
Xiao, B.[Bin],
Liu, C.[Ce],
Yuan, L.[Lu],
Gao, J.F.[Jian-Feng],
Unified Contrastive Learning in Image-Text-Label Space,
CVPR22(19141-19151)
IEEE DOI
2210
Representation learning, Visualization, Image recognition,
Soft sensors, Supervised learning, Transfer learning,
Representation learning
BibRef
Chuang, C.Y.[Ching-Yao],
Hjelm, R.D.[R. Devon],
Wang, X.[Xin],
Vineet, V.[Vibhav],
Joshi, N.[Neel],
Torralba, A.[Antonio],
Jegelka, S.[Stefanie],
Song, Y.[Yale],
Robust Contrastive Learning against Noisy Views,
CVPR22(16649-16660)
IEEE DOI
2210
Representation learning, Loss measurement, Robustness,
Pattern recognition, Noise measurement, Mutual information,
Machine learning
BibRef
Zhang, J.[Junbo],
Ma, K.S.[Kai-Sheng],
Rethinking the Augmentation Module in Contrastive Learning: Learning
Hierarchical Augmentation Invariance with Expanded Views,
CVPR22(16629-16638)
IEEE DOI
2210
Training, Representation learning, Transforms, Benchmark testing,
Data models, Pattern recognition, Representation learning, Self- semi- meta- unsupervised learning
BibRef
Zheng, M.H.[Ming-Hang],
Huang, Y.J.[Yan-Jie],
Chen, Q.C.[Qing-Chao],
Peng, Y.X.[Yu-Xin],
Liu, Y.[Yang],
Weakly Supervised Temporal Sentence Grounding with Gaussian-based
Contrastive Proposal Learning,
CVPR22(15534-15543)
IEEE DOI
2210
Training, Location awareness, Codes, Grounding, Natural languages,
Pattern recognition, Recognition: detection,
Video analysis and understanding
BibRef
Park, J.[Jungin],
Lee, J.Y.[Ji-Young],
Kim, I.J.[Ig-Jae],
Sohn, K.H.[Kwang-Hoon],
Probabilistic Representations for Video Contrastive Learning,
CVPR22(14691-14701)
IEEE DOI
2210
Representation learning, Uncertainty, Stochastic processes,
Self-supervised learning, Gaussian distribution,
Video analysis and understanding
BibRef
Li, S.[Shikun],
Xia, X.B.[Xiao-Bo],
Ge, S.M.[Shi-Ming],
Liu, T.[Tongliang],
Selective-Supervised Contrastive Learning with Noisy Labels,
CVPR22(316-325)
IEEE DOI
2210
Representation learning, Training data, Object detection,
Robustness, Noise measurement, Task analysis, Machine learning,
Self- semi- meta- unsupervised learning
BibRef
Bian, Z.X.[Zhang-Xing],
Jabri, A.[Allan],
Efros, A.A.[Alexei A.],
Owens, A.[Andrew],
Learning Pixel Trajectories with Multiscale Contrastive Random Walks,
CVPR22(6498-6509)
IEEE DOI
2210
Computational modeling, Self-supervised learning,
Object segmentation, Search problems, Trajectory, Motion and tracking
BibRef
Zhu, J.G.[Jiang-Gang],
Wang, Z.[Zheng],
Chen, J.J.[Jing-Jing],
Chen, Y.P.P.[Yi-Ping Phoebe],
Jiang, Y.G.[Yu-Gang],
Balanced Contrastive Learning for Long-Tailed Visual Recognition,
CVPR22(6898-6907)
IEEE DOI
2210
Representation learning, Learning systems, Geometry, Visualization,
Tail, Benchmark testing, Transfer/low-shot/long-tail learning
BibRef
Li, T.H.[Tian-Hong],
Cao, P.[Peng],
Yuan, Y.[Yuan],
Fan, L.J.[Li-Jie],
Yang, Y.Z.[Yu-Zhe],
Feris, R.[Rogerio],
Indyk, P.[Piotr],
Katabi, D.[Dina],
Targeted Supervised Contrastive Learning for Long-Tailed Recognition,
CVPR22(6908-6918)
IEEE DOI
2210
Training, Target recognition, Tail, Performance gain,
Pattern recognition, Task analysis, Transfer/low-shot/long-tail learning
BibRef
Yao, X.[Xufeng],
Bai, Y.[Yang],
Zhang, X.Y.[Xin-Yun],
Zhang, Y.[Yuechen],
Sun, Q.[Qi],
Chen, R.[Ran],
Li, R.[Ruiyu],
Yu, B.[Bei],
PCL: Proxy-based Contrastive Learning for Domain Generalization,
CVPR22(7087-7097)
IEEE DOI
2210
Training, Representation learning, Learning systems,
Computational modeling, Semantics, Benchmark testing,
Representation learning
BibRef
Yu, E.[En],
Li, Z.[Zhuoling],
Han, S.[Shoudong],
Towards Discriminative Representation: Multi-view Trajectory
Contrastive Learning for Online Multi-object Tracking,
CVPR22(8824-8833)
IEEE DOI
2210
Representation learning, Measurement, Bridges, Target tracking,
Costs, Feature extraction, Motion and tracking,
Representation learning
BibRef
Karim, N.[Nazmul],
Rizve, M.N.[Mamshad Nayeem],
Rahnavard, N.[Nazanin],
Mian, A.[Ajmal],
Shah, M.[Mubarak],
UNICON: Combating Label Noise Through Uniform Selection and
Contrastive Learning,
CVPR22(9666-9676)
IEEE DOI
2210
Training, Representation learning, Deep learning, Neural networks,
Semisupervised learning, Probabilistic logic,
Self- semi- meta- Representation learning
BibRef
Ye, Z.S.[Ze-Sheng],
Yao, L.[Lina],
Contrastive Conditional Neural Processes,
CVPR22(9677-9686)
IEEE DOI
2210
Representation learning, Pipelines, Stochastic processes,
Estimation, Probabilistic logic, Hybrid power systems,
Transfer/low-shot/long-tail learning
BibRef
Afham, M.[Mohamed],
Dissanayake, I.[Isuru],
Dissanayake, D.[Dinithi],
Dharmasiri, A.[Amaya],
Thilakarathna, K.[Kanchana],
Rodrigo, R.[Ranga],
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D
Point Cloud Understanding,
CVPR22(9892-9902)
IEEE DOI
2210
Point cloud compression, Representation learning, Training,
Image segmentation, Visualization, Self-supervised learning,
Transfer/low-shot/long-tail learning
BibRef
Pillai, V.[Vipin],
Koohpayegani, S.A.[Soroush Abbasi],
Ouligian, A.[Ashley],
Fong, D.[Dennis],
Pirsiavash, H.[Hamed],
Consistent Explanations by Contrastive Learning,
CVPR22(10203-10212)
IEEE DOI
2210
Training, Heating systems, Deep learning, Annotations,
Neural networks, Self-supervised learning,
Self- semi- meta- unsupervised learning
BibRef
Park, S.[Sungho],
Lee, J.[Jewook],
Lee, P.[Pilhyeon],
Hwang, S.[Sunhee],
Kim, D.[Dohyung],
Byun, H.R.[Hye-Ran],
Fair Contrastive Learning for Facial Attribute Classification,
CVPR22(10379-10388)
IEEE DOI
2210
Representation learning, Degradation, Visualization, Ethics,
Philosophical considerations, Face recognition, Transparency,
privacy and ethics in vision
BibRef
Li, J.C.[Jia-Cheng],
Chen, C.[Chang],
Xiong, Z.W.[Zhi-Wei],
Contextual Outpainting with Object-Level Contrastive Learning,
CVPR22(11441-11450)
IEEE DOI
2210
Bridges, Training, Visualization, Correlation, Shape, Semantics, Layout,
Image and video synthesis and generation, Low-level vision, Visual reasoning
BibRef
Wang, X.[Xuehui],
Zhao, K.[Kai],
Zhang, R.X.[Rui-Xin],
Ding, S.H.[Shou-Hong],
Wang, Y.[Yan],
Shen, W.[Wei],
ContrastMask: Contrastive Learning to Segment Every Thing,
CVPR22(11594-11603)
IEEE DOI
2210
Annotations, Computational modeling, Pattern recognition,
Task analysis, Optimization, Segmentation, grouping and shape analysis
BibRef
Wu, H.[Huisi],
Wang, Z.[Zhaoze],
Song, Y.[Youyi],
Yang, L.[Lin],
Qin, J.[Jing],
Cross-patch Dense Contrastive Learning for Semi-supervised
Segmentation of Cellular Nuclei in Histopathologic Images,
CVPR22(11656-11665)
IEEE DOI
2210
Knowledge engineering, Image segmentation, Shape, Training data,
Feature extraction, Minimization, Entropy, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Meng, J.[Jian],
Yang, L.[Li],
Shin, J.[Jinwoo],
Fan, D.L.[De-Liang],
Seo, J.S.[Jae-Sun],
Contrastive Dual Gating: Learning Sparse Features With Contrastive
Learning,
CVPR22(12247-12255)
IEEE DOI
2210
Heuristic algorithms, Computational modeling,
Supervised learning, Termination of employment,
Self- semi- meta- unsupervised learning
BibRef
Zou, Y.H.[Yun-Hao],
Fu, Y.[Ying],
Estimating Fine-Grained Noise Model via Contrastive Learning,
CVPR22(12672-12681)
IEEE DOI
2210
Computational modeling, Pipelines, Estimation, Training data,
Predictive models, Data models, Sensors,
Low-level vision
BibRef
Chen, M.H.[Ming-Hao],
Wei, F.[Fangyun],
Li, C.[Chong],
Cai, D.[Deng],
Frame-wise Action Representations for Long Videos via Sequence
Contrastive Learning,
CVPR22(13791-13800)
IEEE DOI
2210
Representation learning, Training, Codes, Self-supervised learning,
Gaussian distribution, Pattern recognition,
Self- semi- meta- unsupervised learning
BibRef
Yuan, L.Z.[Liang-Zhe],
Qian, R.[Rui],
Cui, Y.[Yin],
Gong, B.Q.[Bo-Qing],
Schroff, F.[Florian],
Yang, M.H.[Ming-Hsuan],
Adam, H.[Hartwig],
Liu, T.[Ting],
Contextualized Spatio-Temporal Contrastive Learning with
Self-Supervision,
CVPR22(13957-13966)
IEEE DOI
2210
Representation learning, Location awareness,
Self-supervised learning, Transforms, Pattern recognition,
Self- semi- meta- unsupervised learning
BibRef
Wang, J.[Jue],
Bertasius, G.[Gedas],
Tran, D.[Du],
Torresani, L.[Lorenzo],
Long-Short Temporal Contrastive Learning of Video Transformers,
CVPR22(13990-14000)
IEEE DOI
2210
Representation learning, Image recognition,
Benchmark testing, Transformers,
Self- semi- meta- unsupervised learning
BibRef
Yan, J.[Jiexi],
Luo, L.[Lei],
Xu, C.H.[Cheng-Hao],
Deng, C.[Cheng],
Huang, H.[Heng],
Noise Is Also Useful:
Negative Correlation-Steered Latent Contrastive Learning,
CVPR22(31-40)
IEEE DOI
2210
Training, Deep learning, Correlation, Neural networks,
Extraterrestrial measurements, Information filters, Data models,
Self- semi- meta- unsupervised learning
BibRef
Yang, J.W.[Jian-Wei],
Bisk, Y.[Yonatan],
Gao, J.F.[Jian-Feng],
TACo: Token-aware Cascade Contrastive Learning for Video-Text
Alignment,
ICCV21(11542-11552)
IEEE DOI
2203
Location awareness, Representation learning, Visualization,
Protocols, Pipelines, Benchmark testing, Syntactics,
BibRef
Zheng, M.K.[Ming-Kai],
Wang, F.[Fei],
You, S.[Shan],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Wang, X.G.[Xiao-Gang],
Xu, C.[Chang],
Weakly Supervised Contrastive Learning,
ICCV21(10022-10031)
IEEE DOI
2203
Representation learning, Visualization, Head, Transfer learning,
Semisupervised learning, Labeling, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Diba, A.[Ali],
Sharma, V.[Vivek],
Safdari, R.[Reza],
Lotfi, D.[Dariush],
Sarfraz, M.S.[M. Saquib],
Stiefelhagen, R.[Rainer],
Van Gool, L.J.[Luc J.],
Vi2CLR: Video and Image for Visual Contrastive Learning of
Representation,
ICCV21(1482-1492)
IEEE DOI
2203
Representation learning, Visualization, Image recognition,
Pipelines, Transfer learning, Supervised learning,
Representation learning
BibRef
Zhong, Z.[Zhun],
Fini, E.[Enrico],
Roy, S.[Subhankar],
Luo, Z.M.[Zhi-Ming],
Ricci, E.[Elisa],
Sebe, N.[Nicu],
Neighborhood Contrastive Learning for Novel Class Discovery,
CVPR21(10862-10870)
IEEE DOI
2111
Aggregates, Feature extraction,
Pattern recognition, Task analysis
BibRef
Kodama, Y.[Yuto],
Wang, Y.[Yinan],
Kawakami, R.[Rei],
Naemura, T.[Takeshi],
Open-set Recognition with Supervised Contrastive Learning,
MVA21(1-5)
DOI Link
2109
Training, Computer aided instruction,
Training data, Feature extraction, Extraterrestrial measurements, Task analysis
BibRef
Ghosh, A.[Aritra],
Lan, A.[Andrew],
Contrastive Learning Improves Model Robustness Under Label Noise,
LLID21(2697-2702)
IEEE DOI
2109
Training, Visualization, Training data,
Semisupervised learning, Robustness, Pattern recognition
BibRef
Rai, N.[Nishant],
Adeli, E.[Ehsan],
Lee, K.H.[Kuan-Hui],
Gaidon, A.[Adrien],
Niebles, J.C.[Juan Carlos],
CoCon: Cooperative-Contrastive Learning,
HVU21(3379-3388)
IEEE DOI
2109
Visualization, Semantics, Performance gain,
Pattern recognition, Noise measurement, Labeling
BibRef
Xie, E.[Enze],
Ding, J.[Jian],
Wang, W.H.[Wen-Hai],
Zhan, X.H.[Xiao-Hang],
Xu, H.[Hang],
Sun, P.[Peize],
Li, Z.G.[Zhen-Guo],
Luo, P.[Ping],
DetCo: Unsupervised Contrastive Learning for Object Detection,
ICCV21(8372-8381)
IEEE DOI
2203
Object detection, Detectors, Feature extraction, Task analysis,
Image classification, Detection and localization in 2D and 3D
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
Wang, P.[Peng],
Han, K.[Kai],
Wei, X.S.[Xiu-Shen],
Zhang, L.[Lei],
Wang, L.[Lei],
Contrastive Learning based Hybrid Networks for Long-Tailed Image
Classification,
CVPR21(943-952)
IEEE DOI
2111
Network structure being composed of a supervised contrastive loss to
learn image representations and a cross-entropy loss to learn
classifiers.
Training, Memory management,
Graphics processing units, Image representation, Proposals
BibRef
Kuang, H.F.[Hao-Fei],
Zhu, Y.[Yi],
Zhang, Z.[Zhi],
Li, X.Y.[Xin-Yu],
Tighe, J.[Joseph],
Schwertfeger, S.[Sören],
Stachniss, C.[Cyrill],
Li, M.[Mu],
Video Contrastive Learning with Global Context,
CVEU21(3188-3188)
IEEE DOI
2112
Training, Location awareness, Learning systems, Visualization,
Conferences
BibRef
Lee, K.S.[Kwot Sin],
Tran, N.T.[Ngoc-Trung],
Cheung, N.M.[Ngai-Man],
InfoMax-GAN: Improved Adversarial Image Generation via Information
Maximization and Contrastive Learning,
WACV21(3941-3951)
IEEE DOI
2106
Training, Image synthesis, Generative adversarial networks,
Reproducibility of results, Libraries, Generators, Tuning
BibRef
Shao, H.[Huan],
Yuan, Z.Q.[Zhao-Quan],
Peng, X.[Xiao],
Wu, X.[Xiao],
Contrastive Learning in Frequency Domain for Non-I.I.D. Image
Classification,
MMMod21(I:111-122).
Springer DOI
2106
Not Independent and Identically Distributed.
BibRef
Zhu, R.[Rui],
Zhao, B.C.[Bing-Chen],
Liu, J.G.[Jin-Gen],
Sun, Z.L.[Zheng-Long],
Chen, C.W.[Chang Wen],
Improving Contrastive Learning by Visualizing Feature Transformation,
ICCV21(10286-10295)
IEEE DOI
2203
Contrastive learning, which aims at minimizing the distance between
positive pairs while maximizing that of negative ones.
Training, Representation learning, Interpolation, Extrapolation,
Visualization, Computational modeling, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Yüksel, O.K.[Oguz Kaan],
Simsar, E.[Enis],
Er, E.G.[Ezgi Gülperi],
Yanardag, P.[Pinar],
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery
of Interpretable Directions,
ICCV21(14243-14252)
IEEE DOI
2203
Image synthesis, Annotations, Computational modeling, Semantics,
Manuals, Aerospace electronics, Image and video synthesis,
Neural generative models
BibRef
Kinakh, V.[Vitaliy],
Taran, O.[Olga],
Voloshynovskiy, S.[Svyatoslav],
ScatSimCLR: Self-Supervised Contrastive Learning with Pretext Task
Regularization for Small-Scale Datasets,
VIPriors21(1098-1106)
IEEE DOI
2112
Training, Adaptation models, Computational modeling, Estimation
BibRef
Dwibedi, D.[Debidatta],
Aytar, Y.[Yusuf],
Tompson, J.[Jonathan],
Sermanet, P.[Pierre],
Zisserman, A.[Andrew],
With a Little Help from My Friends: Nearest-Neighbor Contrastive
Learning of Visual Representations,
ICCV21(9568-9577)
IEEE DOI
2203
Training, Visualization, Protocols, Transfer learning,
Supervised learning, Semantics, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Cui, J.Q.[Jie-Quan],
Zhong, Z.S.[Zhi-Sheng],
Liu, S.[Shu],
Yu, B.[Bei],
Jia, J.Y.[Jia-Ya],
Parametric Contrastive Learning,
ICCV21(695-704)
IEEE DOI
2203
Adaptation models, Image recognition, Codes, Benchmark testing,
Optimization, Recognition and classification, Representation learning
BibRef
Islam, A.[Ashraful],
Chen, C.F.[Chun-Fu],
Panda, R.[Rameswar],
Karlinsky, L.[Leonid],
Radke, R.[Richard],
Feris, R.[Rogerio],
A Broad Study on the Transferability of Visual Representations with
Contrastive Learning,
ICCV21(8825-8835)
IEEE DOI
2203
Representation learning, Visualization, Adaptation models,
Analytical models, Computational modeling, Transfer learning,
Representation learning
BibRef
Wang, J.[Jin],
Jiang, B.[Bo],
Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs,
GSP-CV21(885-892)
IEEE DOI
2112
Knowledge engineering, Learning systems,
Correlation, Semantics, Benchmark testing
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multiple Kernel Learning, MKL .