14.2.11 Contrastive Learning

Chapter Contents (Back)
Contrastive Learning. Learning. Contrastive learning, which aims at minimizing the distance between positive pairs while maximizing that of negative ones.

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


Feng, C.[Chen], Patras, I.[Ioannis],
Adaptive Soft Contrastive Learning,
ICPR22(2721-2727)
IEEE DOI 2212
Representation learning, Visualization, Memory management, Self-supervised learning, Transforms, Benchmark testing, Entropy 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 .


Last update:Jan 23, 2023 at 16:42:47