When Does Contrastive Visual Representation Learning Work?
Open accessAbstract
Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification. While the particulars of pretraining on ImageNet are now relatively well understood, the field still lacks widely accepted best practices for replicating this success on other datasets. As a first step in this direction, we study contrastive self-supervised learning on four diverse large-scale datasets. By looking through the lenses of data quantity, data domain, data quality, and task granularity, we provide new insights into the necessary conditions for successful self-supervised learning. Our key findings include observations such as: (i) the benefit of additional pretraining data beyond 500k images is modest, (ii) adding pretraining images from another domain does not lead to more general representations, (iii) corrupted pretraining images have a disparate impact on supervised and self-supervised pretraining, and (iv) contrastive learning lags far behind supervised learning on finegrained visual classification tasks.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Performance on the ImageNet and downstream datasets is not always consistent due to the inconstancy of data distribution, data domain, and task objective.
“However, recent research [112], [113] suggested that performance on the ImageNet and downstream datasets is not always consistent due to the inconstancy of data distribution, data domain, and task objective.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [112]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
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 21,793 · see citing works
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · see citing works
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691 · see citing works
Emerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638 · cited 1× · see citing works
“It would also be interesting to explore alternative backbone architectures [7, 17], though after controlling for training settings, ResNet-50 remains competitive with newer architectures [54, 55].”
BackgroundSGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309 · see citing works
Places: A 10 Million Image Database for Scene Recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723009 · Cited by 4,042 · see citing works
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.07733 · Cited by 3,846 · see citing works
Exploring Simple Siamese Representation Learning
Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725 · see citing works
Supervised Contrastive Learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., & Krishnan, D. (2020) · arXiv · doi:10.48550/arxiv.2004.11362 · Cited by 3,569 · see citing works
Zhang, R., Isola, P., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,361 · see citing works
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775 · see citing works
Unsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629 · see citing works
Improved Baselines with Momentum Contrastive LearningPreprint
Chen, X., Fan, H., Girshick, R., & He, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04297 · Cited by 2,579 · see citing works
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09882 · Cited by 2,499 · see citing works
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484 · see citing works
Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv · doi:10.48550/arxiv.1803.07728 · Cited by 2,477 · see citing works
Tian, Y., Krishnan, D., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_45 · Cited by 1,997 · see citing works
Thomée, B., Shamma, D. A., Friedland, G. H., Elizalde, B., Ni, K. S., Poland, D., Borth, D., & Li, L.-J. (2016) · Communications of the ACM · doi:10.1145/2812802 · Cited by 1,632 · see citing works
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2018) · arXiv · doi:10.48550/arxiv.1811.12231 · Cited by 1,490 · cited 1× · see citing works
“One possible explanation is that downsampling destroys texture information, which is known to be a particularly important signal for convolutional neural networks [19, 29].”
BackgroundBig Self-Supervised Models are Strong Semi-Supervised LearnersPreprint
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., & Hinton, G. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10029 · Cited by 1,480 · see citing works
The iNaturalist Species Classification and Detection Dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00914 · Cited by 1,451 · see citing works
Self-citationExploring the Limits of Weakly Supervised Pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_12 · Cited by 1,224 · see citing works
A simple framework for contrastive learning of visual representations
Chen, T. (2024) · TIB Data Manager · doi:10.57702/iuuvgtaz · Cited by 1,140 · see citing works
Data-Efficient Image Recognition with Contrastive Predictive Coding
Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S. M. A., & Oord, A. V. D. (2019) · arXiv · doi:10.48550/arxiv.1905.09272 · Cited by 1,014 · see citing works
Large Batch Training of Convolutional NetworksPreprint
You, Y., Gitman, I., & Ginsburg, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.03888 · Cited by 992 · see citing works
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Zhang, R., Isola, P., & Efros, A. A. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.76 · Cited by 724 · see citing works
Recognition in Terra Incognita
Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679 · see citing works
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 · see citing works
Early Convolutions Help Transformers See BetterPreprint
Xiao, T., Singh, M., Mintun, E., Darrell, T., Dollár, P., & Girshick, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14881 · Cited by 565 · see citing works
Rethinking Pre-training and Self-trainingPreprint
Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E. D., & Le, Q. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06882 · Cited by 547 · see citing works
Unsupervised Representation Learning by Sorting Sequences
Lee, H.-Y., Huang, J., Singh, M., & Yang, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.79 · Cited by 529 · see citing works
Tian, Y., Krishnan, D., & Isola, P. (2019) · arXiv · doi:10.48550/arxiv.1906.05849 · Cited by 465 · see citing works
Exploring Simple Siamese Representation Learning
Chen, X., & He, K. (2020) · arXiv · doi:10.48550/arxiv.2011.10566 · Cited by 381 · see citing works
ResNet strikes back: An improved training procedure in timmPreprint
Wightman, R., Touvron, H., & Jeǵou, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.00476 · Cited by 368 · see citing works
VirTex: Learning Visual Representations from Textual Annotations
Desai, K., & Johnson, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01101 · Cited by 350 · see citing works
Scaling and Benchmarking Self-Supervised Visual Representation Learning
Goyal, P., Mahajan, D. K., Gupta, A., & Misra, I. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00649 · Cited by 344 · see citing works
Geography-Aware Self-Supervised Learning
Ayush, K., Uzkent, B., Meng, C., Tanmay, K., Burke, M., Lobell, D., & Ermon, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01002 · Cited by 288 · see citing works
Contrastive Learning of General-Purpose Audio Representations
Saeed, A., Grangier, D., & Zeghidour, N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413528 · Cited by 237 · see citing works
Fixing the train-test resolution discrepancyPreprint
Touvron, H., Vedaldi, A., Douze, M., & Jeǵou, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06423 · Cited by 209 · see citing works
The Origins and Prevalence of Texture Bias in Convolutional Neural NetworksPreprint
Hermann, K. L., Chen, T., & Kornblith, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09071 · Cited by 177 · see citing works
What Should Not Be Contrastive in Contrastive LearningPreprint
Xiao, T., Wang, X.-L., Efros, A. A., & Darrell, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.05659 · Cited by 147 · see citing works
Self-supervised Pretraining of Visual Features in the Wild
Goyal, P., Caron, M., Lefaudeux, B., Xu, M., Wang, P., Pai, V., Singh, M., Liptchinsky, V., Misra, I., Joulin, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.01988 · Cited by 141 · see citing works
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset BiasesPreprint
Purushwalkam, S., & Gupta, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13916 · Cited by 137 · see citing works
Efficient Visual Pretraining with Contrastive Detection
Hénaff, O. J., Koppula, S., Alayrac, J.-B., van den Oord, A., Vinyals, O., & Carreira, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00993 · Cited by 128 · see citing works
What makes instance discrimination good for transfer learning?
Zhao, N., Wu, Z., Lau, R. W. H., & Lin, S. (2020) · arXiv · doi:10.48550/arxiv.2006.06606 · Cited by 115 · see citing works
Kang, J. S., Fernández-Beltrán, R., Duan, P., Liu, S., & Plaza, A. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3007029 · Cited by 106 · see citing works
Tang, S., Ghorbani, A., Yamashita, R., Rehman, S., Dunnmon, J., Zou, J., & Rubin, D. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-87762-2 · Cited by 88 · see citing works
Intriguing Properties of Contrastive LossesPreprint
Chen, T., Luo, C., & Li, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02803 · Cited by 71 · see citing works
Critical Learning Periods in Deep Neural NetworksPreprint
Achille, A., Rovere, M., & Soatto, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08856 · Cited by 62 · see citing works
MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray ModelsPreprint
Sowrirajan, H., Yang, J., Ng, A. Y., & Rajpurkar, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05352 · Cited by 61 · see citing works
Are all negatives created equal in contrastive instance discrimination?Preprint
Cai, T., Frankle, J., Schwab, D. J., & Morcos, A. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06682 · Cited by 58 · see citing works
Divide and Contrast: Self-supervised Learning from Uncurated Data
Tian, Y., Hénaff, O. J., & van den Oord, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00991 · Cited by 57 · see citing works
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without SupervisionPreprint
Goyal, P., Duval, Q., Seessel, I., Caron, M., Misra, I., Sagun, L., Joulin, A., & Bojanowski, P. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.08360 · Cited by 47 · see citing works
Parametric Instance Classification for Unsupervised Visual Feature LearningPreprint
Cao, Y., Xie, Z., Liu, B., Lin, Y., Zhang, Z., & Hu, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.14618 · Cited by 37 · see citing works
Revisiting Contrastive Methods for Unsupervised Learning of Visual RepresentationsPreprint
Gansbeke, W. V., Vandenhende, S., Georgoulis, S., & Gool, L. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05967 · Cited by 37 · see citing works
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining ParadigmsPreprint
Yang, X., He, X., Liang, Y., Yang, Y., Zhang, S., & Xie, P. (2020) · doi:10.36227/techrxiv.12502298.v1 · Cited by 34 · see citing works
Self-citationConcept Generalization in Visual Representation Learning
Sarıyıldız, M. B., Kalantidis, Y., Larlus, D., & Alahari, K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00949 · Cited by 33 · see citing works
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 · see citing works
Long-Tail Zero and Few-Shot Learning via Contrastive Pretraining on and for Small Data
Rethmeier, N., & Augenstein, I. (2022) · doi:10.3390/cmsf2022003010 · Cited by 20 · see citing works
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining ParadigmsPreprint
Yang, X., He, X., Liang, Y., Yang, Y., Zhang, S., & Xie, P. (2020) · doi:10.36227/techrxiv.12502298 · Cited by 19 · see citing works
Self-citationMeasuring Dataset GranularityPreprint
Cui, Y., Gu, Z., Mahajan, D. K., van der Maaten, L., Belongie, S., & Lim, S.-N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10154 · Cited by 11 · see citing works
Self-citationMoCo-Pretraining Improves Representations and Transferability of Chest X-ray ModelsPreprint
Sowrirajan, H., Yang, J. B., Ng, A. Y., & Rajpurkar, P. (2021) · arXiv (Cornell University) · Cited by 10 · see citing works
Self-Supervised Ranking for Representation LearningPreprint
Varamesh, A., Diba, A., Tuytelaars, T., & Gool, L. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07258 · Cited by 9 · see citing works
Data-Efficient Pretraining via Contrastive Self-SupervisionPreprint
Rethmeier, N., & Augenstein, I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01061 · Cited by 8 · see citing works
The GeoLifeCLEF 2020 DatasetPreprint
Cole, E., Deneu, B., Lorieul, T., Servajean, M., Botella, C., Morris, D., Jojić, N., Bonnet, P., & Joly, A. (2020) · HAL · doi:10.48550/arxiv.2004.04192 · Cited by 5 · see citing works
Self-citationEqCo: Equivalent Rules for Self-supervised Contrastive LearningPreprint
Zhu, B., Huang, J., Li, Z., Zhang, X., & Sun, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01929 · Cited by 4 · see citing works
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining ParadigmsPreprint
Yang, X., He, X., Liang, Y., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04234 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
6 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Linus Ericsson, Henry Gouk, and Timothy M Hospedales. How well do self-supervised models transfer? In CVPR, 2021. 1, 3
- Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Un- supervised representation learning by predicting image rota- tions. In ICLR, 2018. 2
- Bart Thomee, David A Shamma, Gerald Friedland, Ben- jamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. Yfcc100m: The new data in multimedia research. Communications of the ACM, 2016. 3
- Yonglong Tian, Dilip Krishnan, and Phillip Isola. Con- trastive multiview coding. In ECCV, 2020. 2
- Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, and Oisin Mac Aodha. Benchmarking rep- resentation learning for natural world image collections. In CVPR, 2021. 2, 12, 15, 16
Cited by
Sorted by citations
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 · see citing works
Fine-Grained Image Analysis With Deep Learning: A Survey
Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J.-X., Peng, Y.-X., Tang, J.-H., Yang, J., & Belongie, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126648 · Cited by 514 · see citing works
Self-citationFoundation Model for Cancer Imaging Biomarkers
Pai, S., Bontempi, D., Hadzic, I., Prudente, V., Sokač, M., Chaunzwa, T. L., Bernatz, S., Hosny, A., Mak, R. H., Birkbak, N. J., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00807-9 · Cited by 199 · see citing works
Wang, Y., Braham, N. A. A., Xiong, Z., Liu, C., Albrecht, C. M., & Zhu, X. X. (2023) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2023.3281651 · Cited by 141 · see citing works
An Empirical Study of Graph Contrastive LearningPreprint
Zhu, Y., Xu, Y., Liu, Q., & Wu, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01116 · Cited by 89 · see citing works
Large-Scale Unsupervised Semantic Segmentation
Gao, S., Li, Z.-Y., Yang, M.-H., Cheng, M.-M., Han, J., & Torr, P. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3218275 · Cited by 88 · see citing works
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 · see citing works
Tao, C., Qi, J., Guo Zhang, A., Zhu, Q., Lu, W., & Li, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3271312 · Cited by 78 · see citing works
Learning Gait Representation From Massive Unlabelled Walking Videos: A Benchmark
Fan, C., Hou, S., Wang, J., Huang, Y., & Yu, S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3312419 · Cited by 58 · see citing works
Learning multi-modal representations by watching hundreds of surgical video lectures
Yuan, K., Srivastav, V., Yu, T., Lavanchy, J. L., Marescaux, J., Mascagni, P., Navab, N., & Padoy, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103644 · Cited by 58 · see citing works
Zhao, R., Zhu, Y., & Li, Y. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107967 · Cited by 50 · see citing works
From Global to Local: Multi-Scale Out-of-Distribution Detection
Zhang, J., Gao, L., Hao, B., Huang, H., Song, J., & Shen, H. T. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3328478 · Cited by 43 · see citing works
Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges
Payandeh, A., Baghaei, K. T., Fayyazsanavi, P., Ramezani, S. B., Chen, Z., & Rahimi, S. (2023) · IEEE Access · doi:10.1109/access.2023.3335196 · Cited by 41 · see citing works
Domain Knowledge-Guided Self-Supervised Change Detection for Remote Sensing Images
Yan, L., Yang, J., & Wang, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3270498 · Cited by 37 · see citing works
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 · see citing works
Shu, Y., van den Hengel, A., & Liu, L.-Q. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.01096 · Cited by 30 · see citing works
(LC): LiDAR-Camera Loop Constraints for Cross-Modal Place Recognition
Lee, A. J., Song, S., Lim, H., Lee, W., & Myung, H. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3268848 · Cited by 27 · see citing works
Teaching Matters: Investigating the Role of Supervision in Vision Transformers
Walmer, M., Suri, S., Gupta, K., & Shrivastava, A. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00723 · Cited by 26 · see citing works
Plant phenotyping with limited annotation: Doing more with less
Nagasubramanian, K., Jones, S. E., Singh, A. K., Singh, A., Sarkar, S., & Ganapathysubramanian, B. (2022) · The Plant Phenome Journal · doi:10.1002/ppj2.20051 · Cited by 25 · see citing works
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
Stacke, K., Unger, J., Lundström, C. F., & Eilertsen, G. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-f9a1 · Cited by 24 · see citing works
Nielsen, M., Wenderoth, L., Sentker, T., & Werner, R. (2023) · Bioengineering · doi:10.3390/bioengineering10080895 · Cited by 24 · see citing works
Srinidhi, C. L., & Martel, A. L. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00069 · Cited by 23 · see citing works
STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition
Zhu, X., Huang, P.-Y., Liang, J., de Melo, C. M., & Hauptmann, A. G. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00153 · Cited by 23 · see citing works
Clinically Labeled Contrastive Learning for OCT Biomarker Classification
Kokilepersaud, K., Corona, S. T., Prabhushankar, M., AlRegib, G., & Wykoff, C. C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3277789 · Cited by 20 · see citing works
An Evaluation of Self-supervised Pre-training for Skin-Lesion Analysis
Chaves, L., Bissoto, A. E., Junior, E. A. D. V., & Avila, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25069-9_11 · Cited by 20 · see citing works
MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset
Feng, C., & Patras, I. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.01907 · Cited by 35 · see citing works
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
Kim, S., Bae, S., & Yun, S.-Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00728 · Cited by 19 · see citing works
Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping
Ogidi, F., Eramian, M., & Stavness, I. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0037 · Cited by 16 · see citing works
Cai, Z., Lin, L., He, H., Cheng, P., & Tang, X. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3422102 · Cited by 18 · see citing works
From Coarse to Fine-Grained Open-Set Recognition
Lang, N., Snæbjarnarson, V., Cole, E., Aodha, O. M., Igel, C., & Belongie, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01686 · Cited by 18 · see citing works
Self-citationAn Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing
Hu, F.-R., Zhang, C.-L., Guo, J., Wei, X.-S., Zhao, L., Xu, A.-Q., & Gao, L. (2024) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.01671 · Cited by 25 · see citing works
How Severe Is Benchmark-Sensitivity in Video Self-supervised Learning?
Thoker, F. M., Doughty, H., Bagad, P., & Snoek, C. G. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_36 · Cited by 18 · see citing works
An optimal-score-based filter pruning for deep convolutional neural networks
Sawant, S. S., Bauer, J., Erick, F. X., Ingaleshwar, S., Holzer, N., Ramming, A., Lang, E. W., & Götz, T. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03229-5 · Cited by 17 · see citing works
Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning
Walker, B. E., Tucker, A., & Nicolson, N. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2021.806407 · Cited by 16 · see citing works
About Time: Advances, Challenges, and Outlooks of Action Understanding
Stergiou, A., & Poppe, R. W. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02478-4 · Cited by 15 · see citing works
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
Li, D., Ling, H., Kar, A., Acuna, D., Kim, S. W., Kreis, K., Torralba, A. B., & Fidler, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01531 · Cited by 15 · see citing works
On Label Granularity and Object Localization
Cole, E., Wilber, M. J., Horn, G. V., Yang, X., Fornoni, M., Perona, P., Belongie, S., Howard, A., & Aodha, O. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_35 · Cited by 15 · see citing works
Self-citationGwilliam, M., & Shrivastava, A. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00942 · Cited by 26 · see citing works
Instance Segmentation of Shrimp Based on Contrastive Learning
Zhou, H., Kim, S. H., Kim, S. C., Kim, C. W., Kang, S. W., & Kim, H. (2023) · Applied Sciences · doi:10.3390/app13126979 · Cited by 14 · see citing works
A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification
Marks, M., Knott, M., Kondapaneni, N., Cole, E., Defraeye, T., Pérez-Cruz, F., & Perona, P. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-025-02402-w · Cited by 30 · see citing works
Self-citationSelEx: Self-expertise in Fine-Grained Generalized Category Discovery
Rastegar, S., Salehi, M., Asano, Y. M., Doughty, H., & Snoek, C. G. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72897-6_25 · Cited by 13 · see citing works
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization
Bi, Q., Ji, W., Yi, J., Zhan, H., & Xia, G.-S. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3567834 · Cited by 12 · see citing works
MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation
Kalluri, T., Sharma, A., & Chandraker, M. K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_32 · Cited by 12 · see citing works
Dan, R., Li, Y., Wang, Y., Chen, X.-D., Jia, G., Wang, S., Ge, R., Qian, G., Jin, Q., Ye, J., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3271696 · Cited by 11 · see citing works
Chan, T. C. Y., Lin, B., & Saxe, S. (2025) · Manufacturing & Service Operations Management · doi:10.1287/msom.2024.1317 · Cited by 11 · see citing works
SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous DrivingPreprint
Han, J., Liang, X., Xu, H., Chen, K., Hong, L., Ye, C., Zhang, W., Li, Z., Liang, X., & Xu, C. (2021) · arXiv (Cornell University) · Cited by 11 · see citing works
VRL-IQA: Visual Representation Learning for Image Quality Assessment
Aslam, M. A., Xu, W., Ahmed, N., Saleem, G., Amin, T., & Caixue, H. (2023) · IEEE Access · doi:10.1109/access.2023.3340266 · Cited by 10 · see citing works
Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing
Liu, Z., Han, J., Hong, L., Xu, H., Chen, K., Xu, C., & Li, Z. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20079 · Cited by 10 · see citing works
How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition?
Dhekane, S. G., Haresamudram, H., Thukral, M., & Plötz, T. (2023) · Proceedings of the 2023 International Symposium on Wearable Computers · doi:10.1145/3594738.3611366 · Cited by 9 · see citing works
Dual-Dependency Attention Transformer for Fine-Grained Visual Classification
Cui, S., & Hui, B. (2024) · Sensors · doi:10.3390/s24072337 · Cited by 9 · see citing works
On Pretraining Data Diversity for Self-Supervised Learning
Hammoud, H. A. A. K., Das, T., Pizzati, F., Torr, P. H. S., Bibi, A., & Ghanem, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72992-8_4 · Cited by 8 · see citing works
Self-Supervision. Remote Sensing and Abstraction: Representation Learning Across 3 Million Locations
Seneviratne, S., Nice, K. A., Wijnands, J. S., Stevenson, M., & Thompson, J. (2021) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta52665.2021.9647061 · Cited by 8 · see citing works
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationPreprint
Pourmirzaei, M., Montazer, G. A., & Esmaili, F. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4395579 · Cited by 8 · see citing works
Cai, W., Huang, L., & Zou, Z. (2023) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3600100.3623739 · Cited by 7 · see citing works
GeoNet: Benchmarking Unsupervised Adaptation across Geographies
Kalluri, T., Xu, W., & Chandraker, M. K. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01475 · Cited by 7 · see citing works
Spatial-aware multi-modal contrastive learning for RGB-D salient object detection and beyond
Chen, H., Chen, Z., Wu, Y., & Chen, H. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103362 · Cited by 7 · see citing works
Quan, Q., Yao, Q., Zhu, H., & Zhou, S. K. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3436713 · Cited by 7 · see citing works
Wang, Z., Liu, L., Weston, S. R. F., Tian, S., & Li, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73024-5_7 · Cited by 6 · see citing works
Seneviratne, S. (2021) · CLEF (Working Notes) · Cited by 6 · see citing works
LearnMat: Semantic-Aware Self-Supervision Fine-Grained Visual Recognition
Li, S., Cai, Q., Zhang, F. W., Shu, Y., Li, G., Dong, J., Liu, L., & Zhang, D. D. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3671661 · Cited by 5 · see citing works
Sharma, T., Cline, D. E., & Edgington, D. R. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00129 · Cited by 5 · see citing works
Leveraging self-supervised learning for scene classification in child sexual abuse imagery
Valois, P. H. V., Macedo, J., Ribeiro, L. S. F., dos Santos, J. A., & Avila, S. (2025) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2025.301918 · Cited by 5 · see citing works
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look
Zhang, Y. A., Zhu, R., Zhang, S., Zhou, X. Y., Chen, S., & Chen, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651086 · Cited by 4 · see citing works
Huynh, A., Gillespie, L., Lopez-Saucedo, J., Tang, C., Sikand, R., & Expósito‐Alonso, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72989-8_10 · Cited by 4 · see citing works
Trustworthy requirements for foundation models—A comprehensive survey and roadmap
Song, P., Ojo, A., & Curry, E. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113111 · Cited by 4 · see citing works
A survey on design choices for self-supervised learning in computer vision
Wittscher, L. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11506-9 · Cited by 4 · see citing works
PARTICLE: Part Discovery and Contrastive Learning for Fine-grained Recognition
Saha, O., & Maji, S. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00024 · Cited by 4 · see citing works
Shi, J., Wei, Z., Nie, J., & Huang, L. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3618279 · Cited by 4 · see citing works
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
Wei, X. S., Cong, H., Zhang, Z., Peng, J., Chen, G., & Li, J. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995127 · Cited by 4 · see citing works
Chen, Z., Jiang, J., Luo, X., Lei, F., Yuan, X., & Zhan, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130600 · Cited by 3 · see citing works
Tan, B., Wei, X.-S., & Zhao, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00393 · Cited by 3 · see citing works
JLCSR: Joint Learning of Compactness and Separability Representations for Few-Shot Classification
Yang, S., Liu, F., Zheng, S., & Tan, Y. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3297578 · Cited by 3 · see citing works
Adem, A. M., Kant, R., Sarkar, S., Sonia, S., Gongada, T. N., & Eesha (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10182906 · Cited by 3 · see citing works
Cognition-Supervised Saliency Detection: Contrasting EEG Signals and Visual Stimuli
Ma, J., & Ruotsalo, T. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681037 · Cited by 3 · see citing works
Bi-knowledge views recommendation based on user-oriented contrastive learning
Liu, Y., Xuan, H., & Li, B. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00778-0 · Cited by 3 · see citing works
Self-supervised learning for early detection of neurodegenerative diseases with small data
Jiang, H. (2023) · doi:10.32657/10356/166402 · Cited by 3 · see citing works
mRNABench: A curated benchmark for mature mRNA property and function predictionPreprint
Shi, R., Dalal, T., Fradkin, P., Koyyalagunta, D., Chhabria, S., Jung, A., Tam, C. L., Ceyhan, D., Lin, J., Laverty, K. U., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.05.662870 · Cited by 3 · see citing works
Witte, A. V., Lange, S., & Lins, C. (2024) · Industrial Artificial Intelligence · doi:10.1007/s44244-024-00020-y · Cited by 2 · see citing works
RepCo: Replenish sample views with better consistency for contrastive learning
Lei, X., Liu, L., Zhang, Y., Jia, P., Zhang, H., & Zheng, N. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.004 · Cited by 2 · see citing works
Dack, E., Brigato, L., McMurray, M., Fontanellaz, M., Frauenfelder, T., Hoppe, H., Exadaktylos, A. K., Geiser, T., Funke-Chambour, M., Christel, A., et al. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00278 · Cited by 2 · see citing works
Zhong, Y., Tang, H., Chen, J., & Wang, Y.-X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01496 · Cited by 2 · see citing works
Balanced And Discriminative Contrastive Learning For Class-Imbalanced Medical Images
Li, X., Fan, Y., Zheng, H., Gao, J., Wei, X., & Yu, M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447586 · Cited by 2 · see citing works
Robot imitation from multimodal observation with unsupervised cross-modal representation
Xu, X., You, M., Zhou, H., & He, B. (2024) · Robotica · doi:10.1017/s0263574724000626 · Cited by 1 · see citing works
Kondapaneni, N., Perona, P., & Aodha, O. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19806-9_24 · Cited by 1 · see citing works
Self-citationEnhancing 2D Representation Learning with a 3D Prior
Aygün, M. M., Dhar, P., Yan, Z., Aodha, O. M., & Ranjan, R. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00771 · Cited by 1 · see citing works
Self-citationLee, Y., Lee, S., & Hwang, S. (2023) · Applied Sciences · doi:10.3390/app131810493 · Cited by 1 · see citing works
Kalluri, T., Ravichandran, S., & Chandraker, M. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73013-9_12 · Cited by 1 · see citing works
Pre-training for Action Recognition with Automatically Generated Fractal Datasets
Svyezhentsev, D., Retsinas, G., & Maragos, P. A. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02420-8 · Cited by 1 · see citing works
Saliency Can Be All You Need in Contrastive Self-supervised Learning
Kocaman, V., Shir, O. M., Bäck, T., & Belbachir, A. N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20716-7_10 · Cited by 1 · see citing works
Semi-Supervised Learning with Taxonomic Labels
Su, J.-C., & Maji, S. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.45 · Cited by 1 · see citing works
How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?Preprint
Pourmirzaei, M., Montazer, G. A., & Esmaili, F. (2021) · arXiv (Cornell University)
Wei, X.-S. (2023) · Synthesis lectures on computer vision · doi:10.1007/978-3-031-31374-5_6
Self-Supervised Variational Contrastive Learning with Applications to Face Understanding
Yavuz, M. C., & Yanikoglu, B. A. (2024) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg59268.2024.10582001
CF-CAD: A Contrastive Fusion Network For 3D Computer-Aided Design Generative Modeling
Li, X., Chen, H., Lou, Y., & Zhou, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-5555-4_31
Zhao, P., Wang, J., Lu, Z., & Miao, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888879
Taxonomy-Aware Evaluation of Vision–Language Models
Snæbjarnarson, V., Du, K., Stoehr, N., Belongie, S., Cotterell, R., Lang, N., & Frank, S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00851
Self-citationBuatthaisong, N., Slijepcevic, I. V., Scaife, A. M. M., Bowles, M., Hopkins, A., Mohan, D., Shabala, S. S., & Wong, O. I. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1942
One-Shot Cross-Domain Instance Detection With Universal Representation
Feng, C., Cheng, J., Xiao, Y., & Cao, Z. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3616634
A Practical Study of Data Requirements for Self-Supervised Learning in Medical Image Analysis
Hommyo, M., Tsuji, T., Kumagai, S., Shiraishi, K., & Kotoku, J. (2025) · Cureus · doi:10.7759/cureus.98049
Wu, T., Qiao, L., Hong, Y., Su, L., Xu, X., & He, W. (2025) · IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) · doi:10.1109/auteee67053.2025.11322206
- Authors
- Cole, E., Yang, X., Wilber, K., Aodha, O. M., & Belongie, S.
- Affiliations
- California Institute of Technology (US)
- Google (United States) (US)
- The Alan Turing Institute (GB)
- University of Copenhagen (DK)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 01-10
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3160314846
- Cited by
- 105
- References
- 78
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 19 · 2023: 34 · 2022: 13 · 2021: 11
- FWCI
- 12.03
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1745301)
- California Institute of Technology
- Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology
- McGill University
- Danmarks Grundforskningsfond
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- contrastive learning, self-supervised representation learning, domain generalization, image corruptions, fine-grained image classification, ImageNet pre-training, transfer learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
