Fetching the paper…
Reading the bibliography…
We present a retrieval based system for landmark retrieval and recognition challenge.There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching.
Corner detection and curve representation using cubic b-splines
Gerard Medioni and Yoshio Yasumoto · 1987
Earlier work this paper cites.
Video google: A text retrieval approach to object matching in videos
Josef Sivic and Andrew Zisserman · 2003
Earlier work this paper cites.
Visual categorization with bags of keypoints
Gabriella Csurka, Christopher Dance, Lixin Fan, Jutta Willamowski, and Cédric Bray · 2004
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
Earlier work this paper cites.
A comparison of affine region detectors
Krystian Mikolajczyk, Tinne Tuytelaars, Cordelia Schmid, Andrew Zisserman, Jiri Matas, Frederik Schaffalitzky, Timor Kadir, and Luc Van Gool · 2005
Earlier work this paper cites.
Surf: Speeded up robust features
Herbert Bay, Tinne Tuytelaars, and Luc Van Gool · 2006
Earlier work this paper cites.
Total recall: Automatic query expansion with a generative feature model for object retrieval
Ondrej Chum, James Philbin, Josef Sivic, Michael Isard, and Andrew Zisserman · 2007
Earlier work this paper cites.
Aggregating local descriptors into a compact image representation
Hervé Jégou, Matthijs Douze, Cordelia Schmid, and Patrick Pérez · 2010
Cited alongside, same era.
Three things everyone should know to improve object retrieval
Relja Arandjelović and Andrew Zisserman · 2012
Cited alongside, same era.
Negative evidences and co-occurences in image retrieval: The benefit of pca and whitening
Hervé Jégou and Ondřej Chum · 2012
Cited alongside, same era.
Image classification with the fisher vector: Theory and practice
Jorge Sánchez, Florent Perronnin, Thomas Mensink, and Jakob Verbeek · 2013
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Large-scale image retrieval with attentive deep local features
Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Later among the works it cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Later among the works it cites.
Sift meets cnn: A decade survey of instance retrieval
Liang Zheng, Yi Yang, and Qi Tian · 2017
Later among the works it cites.
Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2018
Later among the works it cites.
Squeeze-and-excitation networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Cited alongside, same era.
Jie Hu, Li Shen, and Gang Sun · 2018
Later among the works it cites.
Bag of tricks for image classification with convolutional neural networks
Junyuan Xie, Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, and Mu Li · 2018
Later among the works it cites.