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Recent studies in image retrieval task have shown that ensembling different models and combining multiple global descriptors lead to performance improvement.
Pca-sift: A more distinctive representation for local image descriptors
Y. Ke, R. Sukthankar, et al · 2004
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Distinctive image features from scale-invariant keypoints
D. G. Lowe · 2004
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A theoretical analysis of feature pooling in visual recognition
Y.-L. Boureau, J. Ponce, and Y. LeCun · 2010
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Learning a fine vocabulary
A. Mikulík, M. Perdoch, O. Chum, and J. Matas · 2010
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
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Neural codes for image retrieval
A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Aggregating local deep features for image retrieval
A. Babenko and V. Lempitsky · 2015
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang · 2015
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Fast r-cnn
R. Girshick · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Pairwise geometric matching for large-scale object retrieval
X. Li, M. Larson, and A. Hanjalic · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Particular object retrieval with integral max-pooling of cnn activations
G. Tolias, R. Sicre, and H. Jégou · 2015
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Deep image retrieval: Learning global representations for image search
A. Gordo, J. Almazán, J. Revaud, and D. Larlus · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Cross-dimensional weighting for aggregated deep convolutional features
Y. Kalantidis, C. Mellina, and S. Osindero · 2016
Cited alongside, same era.
Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang · 2016
Cited alongside, same era.
Deep metric learning via lifted structured feature embedding
H. Oh Song, Y. Xiang, S. Jegelka, and S. Savarese · 2016
Cited alongside, same era.
Efficient model averaging for deep neural networks
M. Opitz, H. Possegger, and H. Bischof · 2016
Cited alongside, same era.
Batch feature erasing for person re-identification and beyond
Z. Dai, M. Chen, S. Zhu, and P. Tan · 2018
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Deep metric learning with hierarchical triplet loss
W. Ge · 2018
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Attention-aware generalized mean pooling for image retrieval
Y. Gu, C. Li, and J. Xie · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Attention-based ensemble for deep metric learning
W. Kim, B. Goyal, K. Chawla, J. Lee, and K. Kwon · 2018
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Harmonious attention network for person re-identification
W. Li, X. Zhu, and S. Gong · 2018
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Image search with selective match kernels: aggregation across single and multiple images
G. Tolias, Y. Avrithis, and H. Jégou · 2016
Cited alongside, same era.
End-to-end learning of deep visual representations for image retrieval
A. Gordo, J. Almazan, J. Revaud, and D. Larlus · 2017
Cited alongside, same era.
On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
Cited alongside, same era.
In Defense of the Triplet Loss for Person Re-Identification
A. Hermans*, L. Beyer*, and B. Leibe · 2017
Cited alongside, same era.
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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
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
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Fine-tuning cnn image retrieval with no human annotation
F. Radenović, G. Tolias, and O. Chum · 2018
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Learning discriminative features with multiple granularities for person re-identification
G. Wang, Y. Yuan, X. Chen, J. Li, and X. Zhou · 2018
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Weighted generalized mean pooling for deep image retrieval
X. Wu, G. Irie, K. Hiramatsu, and K. Kashino · 2018
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Deep randomized ensembles for metric learning
H. Xuan, R. Souvenir, and R. Pless · 2018
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Hard-aware point-to-set deep metric for person re-identification
R. Yu, Z. Dou, S. Bai, Z. Zhang, Y. Xu, and X. Bai · 2018
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Making classification competitive for deep metric learning
A. Zhai and H.-Y. Wu · 2018
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X. Zhang, F. X. Yu, S. Karaman, W. Zhang, and S.-F. Chang · 2018
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Binary ensemble neural network: More bits per network or more networks per bit?
S. Zhu, X. Dong, and H. Su · 2018
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https://www.kaggle.com/c/landmark-retrieval-2019
Google landmark retrieval 2019 · 2019
Closest in time.
2nd place and 2nd place solution to kaggle landmark recognition andretrieval competition 2019
K. Chen, C. Cui, Y. Du, X. Meng, and H. Ren · 2019
Closest in time.
Large-scale landmark retrieval/recognition under a noisy and diverse dataset
K. Ozaki and S. Yokoo · 2019
Closest in time.
Visualizing deep similarity networks
A. Stylianou, R. Souvenir, and R. Pless · 2019
Closest in time.