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We introduce RP2K, a new large-scale retail product dataset for fine-grained image classification.
Soil-47, the surrey object image library, centre for vision, speach and signal processing, univerisity of surrey
J Burianek, A Ahmadyfard, and J Kittler · 2002
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Product placements: How to measure their impact
Sharmistha Law and Kathryn A Braun-LaTour · 2004
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Learning attentive pairwise interaction for fine-grained classification
Peiqin Zhuang, Yali Wang, and Yu Qiao · 2006
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Michele Merler, Carolina Galleguillos, and Serge Belongie · 2007
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Automatic fruit and vegetable classification from images
Anderson Rocha, Daniel C Hauagge, Jacques Wainer, and Siome Goldenstein · 2010
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Birdsnap: Large-scale fine-grained visual categorization of birds
Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L Alexander, David W Jacobs, and Peter N Belhumeur · 2014
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Recognizing products: A per-exemplar multi-label image classification approach
Marian George and Christian Floerkemeier · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Toward retail product recognition on grocery shelves
Gül Varol and Rıdvan Salih Kuzu · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Precise detection in densely packed scenes
Eran Goldman, Roei Herzig, Aviv Eisenschtat, Jacob Goldberger, and Tal Hassner · 2019
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Take goods from shelves: A dataset for class-incremental object detection
Yu Hao, Yanwei Fu, and Yu-Gang Jiang · 2019
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Generating multiple objects at spatially distinct locations
Tobias Hinz, Stefan Heinrich, and Stefan Wermter · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Content and style disentanglement for artistic style transfer
Dmytro Kotovenko, Artsiom Sanakoyeu, Sabine Lang, and Bjorn Ommer · 2019
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Fine-grained recognition of thousands of object categories with single-example training
Leonid Karlinsky, Joseph Shtok, Yochay Tzur, and Asaf Tzadok · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Patrick Follmann, Tobias Bottger, Philipp Hartinger, Rebecca Konig, and Markus Ulrich · 2018
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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Cosmogan: creating high-fidelity weak lensing convergence maps using generative adversarial networks
Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, and Jan M Kratochvil · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Ke-gan: Knowledge embedded generative adversarial networks for semi-supervised scene parsing
Mengshi Qi, Yunhong Wang, Jie Qin, and Annan Li · 2019
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A comprehensive survey on computer vision based approaches for automatic identification of products in retail store
Bikash Santra and Dipti Prasad Mukherjee · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Image synthesis from reconfigurable layout and style
Wei Sun and Tianfu Wu · 2019
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Rpc: A large-scale retail product checkout dataset
Xiu-Shen Wei, Quan Cui, Lei Yang, Peng Wang, and Lingqiao Liu · 2019
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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One man’s trash is another man’s treasure: Resisting adversarial examples by adversarial examples
Chang Xiao and Changxi Zheng · 2020
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