A simple framework for contrastive learning of visual representations

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472

Learning to Prompt for Vision-Language Models

Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01653-1 · Cited by 3,304

SimCSE: Simple Contrastive Learning of Sentence Embeddings

Gao, T., Yao, X., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.552 · Cited by 2,851

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y., Li, Z., & Duerig, T. (2021) · arXiv · doi:10.48550/arxiv.2102.05918 · Cited by 1,977

An Empirical Study of Training Self-Supervised Vision Transformers

Chen, X., Xie, S., & He, K. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00950 · Cited by 1,648

Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

Li, J., Selvaraju, R. R., Gotmare, A. D., Joty, S., Xiong, C., & Hoi, S. (2021) · arXiv · doi:10.48550/arxiv.2107.07651 · Cited by 1,078

Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

Li, B., Li, Y., & Eliceiri, K. W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01409 · Cited by 957

ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Kim, W., Son, B., & Kim, I. (2021) · arXiv · doi:10.48550/arxiv.2102.03334 · Cited by 951

Contrastive Representation Learning: A Framework and Review

Le-Khac, P. H., Healy, G., & Smeaton, A. F. (2020) · IEEE Access · doi:10.1109/access.2020.3031549 · Cited by 862

TS2Vec: Towards Universal Representation of Time Series

Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., & Xu, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20881 · Cited by 850

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

Wang, X., Zhang, R., Shen, C., Kong, T., & Li, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00304 · Cited by 660

Prototypical Contrastive Learning of Unsupervised Representations

Li, J., Zhou, P., Xiong, C., & Hoi, S. C. H. (2020) · arXiv · doi:10.48550/arxiv.2005.04966 · Cited by 611

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., & Gool, L. V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00721 · Cited by 604

XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

Babu, A., Wang, C., Tjandra, A., Lakhotia, K., Xu, Q., Goyal, N., Singh, K., von Platen, P., Saraf, Y., Pino, J., et al. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-143 · Cited by 586

CogView: Mastering Text-to-Image Generation via Transformers

Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al. (2021) · arXiv · doi:10.48550/arxiv.2105.13290 · Cited by 450

Untitled work

(2021) · International Conference on Learning Representations · Cited by 449

Prototype Augmentation and Self-Supervision for Incremental Learning

Zhu, F., Zhang, X.-Y., Wang, C., Yin, F., & Liu, C.-L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00581 · Cited by 427

Robust fine-tuning of zero-shot models

Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00780 · Cited by 428

With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., & Zisserman, A. (2021) · arXiv · doi:10.48550/arxiv.2104.14548 · Cited by 405

Sustainable AI: Environmental Implications, Challenges and Opportunities

Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Chang, G., Behram, F. A., Huang, J., Bai, C., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.00364 · Cited by 405

With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00945 · Cited by 404

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

Xie, Z., Lin, Y., Zhang, Z., Cao, Y., Lin, S., & Hu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01641 · Cited by 370

DocFormer: End-to-End Transformer for Document Understanding

Appalaraju, S., Jasani, B., Kota, B. U., Xie, Y., & Manmatha, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00103 · Cited by 363

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Manas, O., Lacoste, A., Giro-i-Nieto, X., Vazquez, D., & Rodriguez, P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00928 · Cited by 354

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Tack, J., Mo, S., Jeong, J., & Shin, J. (2020) · arXiv · doi:10.48550/arxiv.2007.08176 · Cited by 351

Reinforcement Learning with Augmented Data

Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., & Srinivas, A. (2020) · arXiv · doi:10.48550/arxiv.2004.14990 · Cited by 299

A Survey on Semi-, Self- and Unsupervised Learning for Image Classification

Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R. (2021) · IEEE Access · doi:10.1109/access.2021.3084358 · Cited by 246

A Survey on Deep Hashing Methods

Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., & Hua, X.-S. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532624 · Cited by 241

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals

Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00990 · Cited by 231

BEVT: BERT Pretraining of Video Transformers

Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y.-G., Zhou, L., & Yuan, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01432 · Cited by 234

CLIPort: What and Where Pathways for Robotic Manipulation

Shridhar, M., Manuelli, L., & Fox, D. (2021) · arXiv · doi:10.48550/arxiv.2109.12098 · Cited by 223

Learning Invariant Representations for Reinforcement Learning without Reconstruction

Zhang, A., McAllister, R., Calandra, R., Gal, Y., & Levine, S. (2020) · arXiv · doi:10.48550/arxiv.2006.10742 · Cited by 165

Revisiting the Calibration of Modern Neural Networks

Minderer, M., Djolonga, J., Romijnders, R., Hubis, F., Zhai, X., Houlsby, N., Tran, D., & Lucic, M. (2021) · arXiv · doi:10.48550/arxiv.2106.07998 · Cited by 160

Contrastive Code Representation Learning

Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J. E., & Stoica, I. (2020) · arXiv · doi:10.48550/arxiv.2007.04973 · Cited by 129

Contrastive Code Representation Learning

Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J., & Stoica, I. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.482 · Cited by 129

When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Chen, X., Hsieh, C.-J., & Gong, B. (2021) · arXiv · doi:10.48550/arxiv.2106.01548 · Cited by 128

Are Convolutional Neural Networks or Transformers more like human vision?

Tuli, S., Dasgupta, I., Grant, E., & Griffiths, T. L. (2021) · arXiv · doi:10.48550/arxiv.2105.07197 · Cited by 119

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., & Sabokrou, M. (2021) · arXiv · doi:10.48550/arxiv.2110.14051 · Cited by 118

Hurdles to Progress in Long-form Question Answering

Krishna, K., Roy, A., & Iyyer, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.393 · Cited by 114

When Does Contrastive Visual Representation Learning Work?

Cole, E., Yang, X., Wilber, K., Aodha, O. M., & Belongie, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01434 · Cited by 105

A Survey of Self-Supervised and Few-Shot Object Detection

Huang, G., Laradji, I., Vazquez, D., Lacoste-Julien, S., & Rodriguez, P. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3199617 · Cited by 100

A Good Image Generator Is What You Need for High-Resolution Video Synthesis

Tian, Y., Ren, J., Chai, M., Olszewski, K., Peng, X., Metaxas, D. N., & Tulyakov, S. (2021) · arXiv · doi:10.48550/arxiv.2104.15069 · Cited by 88

Contrastive Self-supervised Learning for Sensor-based Human Activity Recognition

Khaertdinov, B., Ghaleb, E., & Asteriadis, S. (2021) · 2021 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484410 · Cited by 95

Self-supervised Learning is More Robust to Dataset Imbalance

Liu, H., HaoChen, J. Z., Gaidon, A., & Ma, T. (2021) · arXiv · doi:10.48550/arxiv.2110.05025 · Cited by 84

DualNet: Continual Learning, Fast and Slow

Pham, Q., Liu, C., & Hoi, S. (2021) · arXiv · doi:10.48550/arxiv.2110.00175 · Cited by 84

ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics

Taleb, A., Kirchler, M., Monti, R., & Lippert, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02024 · Cited by 83

Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification

Dufumier, B., Gori, P., Victor, J., Grigis, A., Wessa, M., Brambilla, P., Favre, P., Polosan, M., McDonald, C., Piguet, C. M., et al. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87196-3_6 · Cited by 82

Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions

Guha Roy, A., Ren, J., Azizi, S., Loh, A., Natarajan, V., Mustafa, B., Pawlowski, N., Freyberg, J., Liu, Y., Beaver, Z., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102274 · Cited by 77

Radar Camera Fusion via Representation Learning in Autonomous Driving

Dong, X., Zhuang, B., Mao, Y., & Liu, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00183 · Cited by 62

Fine-grained Angular Contrastive Learning with Coarse Labels

Bukchin, G., Schwartz, E., Saenko, K., Shahar, O., Feris, R., Giryes, R., & Karlinsky, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00862 · Cited by 55

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

HaoChen, J. Z., Wei, C., Gaidon, A., & Ma, T. (2021) · arXiv · doi:10.48550/arxiv.2106.04156 · Cited by 53

A Deep Learning Approach for Histopathological Diagnosis of Onychomycosis: Not Inferior to Analogue Diagnosis by Histopathologists

Decroos, F., Springenberg, S., Lang, T., Päpper, M., Zapf, A., Metze, D., Steinkraus, V, & Böer-Auer, A. (2021) · Acta Dermato Venereologica · doi:10.2340/00015555-3893 · Cited by 51

BYOL works even without batch statistics

Richemond, P. H., Grill, J.-B., Altché, F., Tallec, C., Strub, F., Brock, A., Smith, S., De, S., Pascanu, R., Piot, B., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.10241 · Cited by 51

Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation

Yoa, S., Lee, S., Kim, C., & Kim, H. J. (2021) · IEEE Access · doi:10.1109/access.2021.3124525 · Cited by 38

Stock Trend Prediction with Multi-granularity Data: A Contrastive Learning Approach with Adaptive Fusion

Hou, M., Xu, C., Liu, Y., Liu, W., Bian, J., Wu, L., Li, Z., Chen, E., & Liu, T.-Y. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3482483 · Cited by 35

Contrasting Contrastive Self-Supervised Representation Learning Pipelines

Kotar, K., Ilharco, G., Schmidt, L., Ehsani, K., & Mottaghi, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00980 · Cited by 28

Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency

Hyun, S., Kim, J., & Heo, J.-P. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01068 · Cited by 16

Procedural Generalization by Planning with Self-Supervised World Models

Anand, A., Walker, J., Li, Y., Vértes, E., Schrittwieser, J., Ozair, S., Weber, T., & Hamrick, J. B. (2021) · arXiv · doi:10.48550/arxiv.2111.01587 · Cited by 5