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Transformers have shown outstanding results for natural language understanding and, more recently, for image classification.
Sample estimate of the entropy of a random vector
Kozachenko, L. and Leonenko, N. N · 1987
Earlier work this paper cites.
Neighbourhood components analysis
Goldberger, J., Hinton, G. E., Roweis, S., and Salakhutdinov, R. R · 2005
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., and LeCun, Y · 2006
Earlier work this paper cites.
Object retrieval with large vocabularies and fast spatial matching
Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A · 2007
Earlier work this paper cites.
Lost in quantization: Improving particular object retrieval in large scale image databases
Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification
Weinberger, K. Q. and Saul, L. K · 2009
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
Neural codes for image retrieval
Babenko, A., Slesarev, A., Chigorin, A., and Lempitsky, V · 2014
Earlier work this paper cites.
Aggregating deep convolutional features for image retrieval
Babenko, A. and Lempitsky, V · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
From single image query to detailed 3d reconstruction
Schonberger, J. L., Radenovic, F., Chum, O., and Frahm, J.-M · 2015
Earlier work this paper cites.
Particular object retrieval with integral max-pooling of cnn activations
Tolias, G., Sicre, R., and Jégou, H · 2015
Earlier work this paper cites.
Deep image retrieval: Learning global representations for image search
Gordo, A., Almazán, J., Revaud, J., and Larlus, D · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Liu, Z., Luo, P., Qiu, S., Wang, X., and Tang, X · 2016
Earlier work this paper cites.
Deep metric learning via lifted structured feature embedding
Oh Song, H., Xiang, Y., Jegelka, S., and Savarese, S · 2016
Earlier work this paper cites.
Improved deep metric learning with multi-class n-pair loss objective
Sohn, K · 2016
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Earlier work this paper cites.
No fuss distance metric learning using proxies
Movshovitz-Attias, Y., Toshev, A., Leung, T. K., Ioffe, S., and Singh, S · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Sampling matters in deep embedding learning
Wu, C.-Y., Manmatha, R., Smola, A. J., and Krahenbuhl, P · 2017
Cited alongside, same era.
Learning spread-out local feature descriptors
Zhang, X., Yu, F. X., Kumar, S., and Chang, S.-F · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Deep metric learning with hierarchical triplet loss
Ge, W · 2018
Mic: Mining interclass characteristics for improved metric learning
Roth, K., Brattoli, B., and Ommer, B · 2019
Later among the works it cites.
Spreading vectors for similarity search
Sablayrolles, A., Douze, M., Schmid, C., and Jégou, H · 2019
Later among the works it cites.
Stochastic class-based hard example mining for deep metric learning
Suh, Y., Han, B., Kim, W., and Lee, K. M · 2019
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Multi-similarity loss with general pair weighting for deep metric learning
Wang, X., Han, X., Huang, W., Dong, D., and Scott, M. R · 2019
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Pytorch image models
Wightman, R · 2019
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Regularface: Deep face recognition via exclusive regularization
Zhao, K., Xu, J., and Cheng, M.-M · 2019
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Cited alongside, same era.
Attention-based ensemble for deep metric learning
Kim, W., Goyal, B., Chawla, K., Lee, J., and Kwon, K · 2018
Cited alongside, same era.
Deep metric learning with bier: Boosting independent embeddings robustly
Opitz, M., Waltner, G., Possegger, H., and Bischof, H · 2018
Cited alongside, same era.
Improving language understanding with unsupervised learning
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
Cited alongside, same era.
Non-local neural networks
Wang, X., Girshick, R., Gupta, A., and He, K · 2018
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Improving generalization via scalable neighborhood component analysis
Wu, Z., Efros, A. A., and Yu, S · 2018
Cited alongside, same era.
Classification is a strong baseline for deep metric learning
Zhai, A. and Wu, H.-Y · 2018
Cited alongside, same era.
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Groknet: Unified computer vision model trunk and embeddings for commerce
Bell, S., Liu, Y., Alsheikh, S., Tang, Y., Pizzi, E., Henning, M., Singh, K., Parkhi, O., and Borisyuk, F · 2020
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A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Boudiaf, M., Rony, J., Ziko, I. M., Granger, E., Pedersoli, M., Piantanida, P., and Ayed, I. B · 2020
Later among the works it cites.
End-to-end object detection with transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S · 2020
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Generative pretraining from pixels
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., and Sutskever, I · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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A metric learning reality check
Musgrave, K., Belongie, S., and Lim, S.-N · 2020
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Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis
Teh, E. W., DeVries, T., and Taylor, G. W · 2020
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Training data-efficient image transformers and distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2020
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Wang, T. and Isola, P · 2020
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Cross-batch memory for embedding learning
Wang, X., Zhang, H., Huang, W., and Scott, M. R · 2020
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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 · 2021
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Investigating the vision transformer model for image retrieval tasks
Gkelios, S., Boutalis, Y., and Chatzichristofis, S. A · 2021
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