Fetching the paper…
Reading the bibliography…
With the rapid development of electronic commerce, the way of shopping has experienced a revolutionary evolution.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2015
Earlier work this paper cites.
A deep learning pipeline for product recognition on store shelves
Alessio Tonioni, Eugenio Serra, and Luigi Di Stefano · 2018
Earlier work this paper cites.
Learning from imbalanced data sets with weighted cross-entropy function
Yuri Sousa Aurelio, Gustavo Matheus de Almeida, Cristiano Leite de Castro, and Antonio Padua Braga · 2019
Cited alongside, same era.
Destruction and construction learning for fine-grained image recognition
Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei · 2019
Cited alongside, same era.
Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
Cited alongside, same era.
Repair: Removing representation bias by dataset resampling
Yi Li and Nuno Vasconcelos · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Later among the works it cites.
Diffnet: A learning to compare deep network for product recognition
Bin Hu, Nuoya Zhou, Qiang Zhou, Xinggang Wang, and Wenyu Liu · 2020
Closest in time.
Look-into-object: Self-supervised structure modeling for object recognition
Mohan Zhou, Yalong Bai, Wei Zhang, Tiejun Zhao, and Tao Mei · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…