Fetching the paper…
Reading the bibliography…
Contrastive Language Image Pretraining (CLIP) has received widespread attention, since its learned representations can be transferred well to various downstream tasks.
M. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in Sixth Indian Conference on Computer Vision, Graphics & Image Processing, ICVGIP 2008, Bhubaneswar, India, 16-19 December 2008
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA
2009
Earlier work this paper cites.
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “The german traffic sign recognition benchmark: A multi-class classification competition,” in The 2011 International Joint Conference on Neural Networks, IJCNN 2011, San Jose, California, USA, July 31 - August 5, 2011
2011
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar, “Cats and dogs,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16-21, 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in 2013 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2013, Sydney, Australia, December 1-8, 2013
2013
Earlier work this paper cites.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014
2014
Earlier work this paper cites.
L. Bossard, M. Guillaumin, and L. V. Gool, “Food-101 - mining discriminative components with random forests,” in Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI
2014
Earlier work this paper cites.
T. Berg, J. Liu, S. W. Lee, M. L. Alexander, D. W. Jacobs, and P. N. Belhumeur, “Birdsnap: Large-scale fine-grained visual categorization of birds,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014
2014
Earlier work this paper cites.
G. E. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” CoRR
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L. Li, “YFCC100M: the new data in multimedia research,” Commun. ACM
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA
2017
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “SGDR: stochastic gradient descent with warm restarts,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIV
2018
Earlier work this paper cites.
P. Micikevicius, S. Narang, J. Alben, G. F. Diamos, E. Elsen, D. García, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, and H. Wu, “Mixed precision training,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
2018
Earlier work this paper cites.
P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers
2018
Earlier work this paper cites.
L. Yu, V. O. Yazici, X. Liu, J. van de Weijer, Y. Cheng, and A. Ramisa, “Learning metrics from teachers: Compact networks for image embedding,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Lu, D. Batra, D. Parikh, and S. Lee, “Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada
2019
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers)
2019
Earlier work this paper cites.
W. Park, D. Kim, Y. Lu, and M. Cho, “Relational knowledge distillation,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019
2019
Cited alongside, same era.
T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, and M. Li, “Bag of tricks for image classification with convolutional neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019
2019
Cited alongside, same era.
R. Zhang, “Making convolutional networks shift-invariant again,” in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA
2019
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,” IEEE Trans. Pattern Anal. Mach. Intell
2021
Later among the works it cites.
X. Chen and K. He, “Exploring simple siamese representation learning,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021
2021
Later among the works it cites.
D. Dwibedi, Y. Aytar, J. Tompson, P. Sermanet, and A. Zisserman, “With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,” in 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Johnson, M. Douze, and H. Jégou, “Billion-scale similarity search with GPUs,” IEEE Transactions on Big Data
2019
Cited alongside, same era.
T. Wang and P. Isola, “Understanding contrastive representation learning through alignment and uniformity on the hypersphere,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton, “A simple framework for contrastive learning of visual representations,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
2020
Cited alongside, same era.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Cited alongside, same era.
Y. M. Asano, C. Rupprecht, and A. Vedaldi, “Self-labelling via simultaneous clustering and representation learning,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020
2020
Cited alongside, same era.
H. Alwassel, D. Mahajan, B. Korbar, L. Torresani, B. Ghanem, and D. Tran, “Self-supervised learning by cross-modal audio-video clustering,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Cited alongside, same era.
Y. M. Asano, M. Patrick, C. Rupprecht, and A. Vedaldi, “Labelling unlabelled videos from scratch with multi-modal self-supervision,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Cited alongside, same era.
Y. Chen, L. Li, L. Yu, A. E. Kholy, F. Ahmed, Z. Gan, Y. Cheng, and J. Liu, “UNITER: universal image-text representation learning,” in Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXX
2020
Cited alongside, same era.
2021
Later among the works it cites.
If you use this software, please cite it as below
G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V. Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, and L. Schmidt, “Openclip,” July 2021 · 2021
Later among the works it cites.
2021
Later among the works it cites.
J. Xie, X. Zhan, Z. Liu, Y. S. Ong, and C. C. Loy, “Unsupervised object-level representation learning from scene images,” in Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual
2021
Later among the works it cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
S. Lin, C. Liu, P. Zhou, Z.-Y. Hu, S. Wang, R. Zhao, Y. Zheng, L. Lin, E. Xing, and X. Liang, “Prototypical graph contrastive learning,” IEEE Transactions on Neural Networks and Learning Systems
2022
Closest in time.
R.-Q. Wang, X.-Y. Zhang, and C.-L. Liu, “Meta-prototypical learning for domain-agnostic few-shot recognition,” IEEE Transactions on Neural Networks and Learning Systems
2022
Closest in time.
S.-J. Peng, Y. He, X. Liu, Y.-m. Cheung, X. Xu, and Z. Cui, “Relation-aggregated cross-graph correlation learning for fine-grained image–text retrieval,” IEEE Transactions on Neural Networks and Learning Systems
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
J. T. Ash, S. Goel, A. Krishnamurthy, and D. Misra, “Investigating the role of negatives in contrastive representation learning,” in International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event
2022
Closest in time.
2022
Closest in time.
D. Chen, J. Liu, W. Dai, and B. Wang, “Visual instruction tuning with polite flamingo,” CoRR
2023
Closest in time.
Y. Liu, Y. Zhang, Y. Wang, F. Hou, J. Yuan, J. Tian, Y. Zhang, Z. Shi, J. Fan, and Z. He, “A survey of visual transformers,” IEEE Transactions on Neural Networks and Learning Systems
2023
Closest in time.
2023
Closest in time.
W. Tu, S. Zhou, X. Liu, C. Ge, Z. Cai, and Y. Liu, “Hierarchically contrastive hard sample mining for graph self-supervised pretraining,” IEEE Transactions on Neural Networks and Learning Systems
2023
Closest in time.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.,” Journal of machine learning research
2023
Closest in time.