Fetching the paper…
Reading the bibliography…
The Google-Landmarks-v2 dataset is the biggest worldwide landmarks dataset characterized by a large magnitude of noisiness and diversity.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
M. A. Fischler and R. C. Bolles · 1981
Earlier work this paper cites.
Object retrieval with large vocabularies and fast spatial matching
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
A family of contextual measures of similarity between distributions with application to image retrieval
F. Perronnin, Y. Liu, and J.-M. Renders · 2009
Earlier work this paper cites.
Three things everyone should know to improve object retrieval
R. Arandjelovic and A. Zisserman · 2012
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Compact bilinear pooling
Y. Gao, O. Beijbom, N. Zhang, and T. Darrell · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
End-to-end learning of deep visual representations for image retrieval
A. Gordo, J. Almazán, J. Revaud, and D. Larlus · 2017
Cited alongside, same era.
Billion-scale similarity search with gpus
J. Johnson, M. Douze, and H. Jégou · 2017
Cited alongside, same era.
SGDR: stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
Cited alongside, same era.
Large-scale image retrieval with attentive deep local features
H. Noh, A. Araujo, J. Sim, T. Weyand, and B. Han · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, and A. Lerer · 2017
Cited alongside, same era.
Arcface: Additive angular margin loss for deep face recognition
Regional maximum activations of convolutions with attention for cross-domain beauty and personal care product retrieval
Z. Lin, Z. Yang, F. Huang, and J. Chen · 2018
Later among the works it cites.
Revisiting oxford and paris: Large-scale image retrieval benchmarking
F. Radenović, A. Iscen, G. Tolias, Y. Avrithis, and O. Chum · 2018
Later among the works it cites.
Fine-tuning cnn image retrieval with no human annotation
F. Radenovic, G. Tolias, and O. Chum · 2018
Later among the works it cites.
Fishnet: A versatile backbone for image, region, and pixel level prediction
S. Sun, J. Pang, J. Shi, S. Yi, and W. Ouyang · 2018
Later among the works it cites.
Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu · 2018
Later among the works it cites.
Explore-exploit graph traversal for image retrieval
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Deng, J. Guo, and S. Zafeiriou · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Cited alongside, same era.
C. Chang, G. Yu, C. Liu, and M. Volkovs · 2019
Closest in time.
Efficient image retrieval via decoupling diffusion into online and offline processing
F. Yang, R. Hinami, Y. Matsui, S. Ly, and S. Satoh · 2019
Closest in time.