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Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
Earlier work this paper cites.
Towards automatic discovery of object categories
Markus Weber, Max Welling, and Pietro Perona · 2000
Earlier work this paper cites.
Discovering objects and their location in images
Josef Sivic, Bryan Russell, Alexei Efros, Andrew Zisserman, and William Freeman · 2005
Earlier work this paper cites.
Unsupervised learning of categories from sets of partially matching image features
Kristen Grauman and Trevor Darrell · 2006
Earlier work this paper cites.
Using multiple segmentations to discover objects and their extent in image collections
Bryan Russell, William Freeman, Alexei Efros, Josef Sivic, and Andrew Zisserman · 2006
Earlier work this paper cites.
The PASCAL visual object classes challenge 2007 (VOC2007) results, 2007
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2007
Earlier work this paper cites.
Non-negative matrix factorisation for object class discovery and image auto-annotation
Jiayu Tang and Paul H Lewis · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Unsupervised detection of regions of interest using iterative link analysis
Gunhee Kim and Antonio Torralba · 2009
Earlier work this paper cites.
Measuring the objectness of image windows
Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari · 2012
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman · 2012
Earlier work this paper cites.
Unsupervised object class discovery via saliency-guided multiple class learning
Jun-Yan Zhu, Jiajun Wu, Yan Xu, Eric Chang, and Zhuowen Tu · 2012
Earlier work this paper cites.
Selective search for object recognition
Jasper Uijlings, Karin van de Sande, Theo Gevers, and Arnold Smeulders · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and Lawrence Zitnick · 2014
Earlier work this paper cites.
Edge boxes: Locating object proposals from edges
Lawrence Zitnick and Piotr Dollár · 2014
Earlier work this paper cites.
Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals
Minsu Cho, Suha Kwak, Cordelia Schmid, and Jean Ponce · 2015
Earlier work this paper cites.
Fast R-CNN
Ross Girshick · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Mining and-or graphs for graph matching and object discovery
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2015
Earlier work this paper cites.
Object detectors emerge in deep scene cnns
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2015
Earlier work this paper cites.
Cliquecnn: Deep unsupervised exemplar learning
Miguel A Bautista, Artsiom Sanakoyeu, Ekaterina Sutter, and Björn Ommer · 2016
Earlier work this paper cites.
Self-taught object localization with deep networks
A Bergamo, L Bazzani, D Anguelov, and L Torresani · 2016
Earlier work this paper cites.
Weakly supervised deep detection networks
Hakan Bilen and Andrea Vedaldi · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 2017
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Deep self-taught learning for weakly supervised object localization
Zequn Jie, Yunchao Wei, Xiaojie Jin, Jiashi Feng, and Wei Liu · 2017
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Data distillation: Towards omni-supervised learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Instance-aware, context-focused, and memory-efficient weakly supervised object detection
Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Yong Jae Lee, Alexander G. Schwing, and Jan Kautz · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Toward unsupervised, multi-object discovery in large-scale image collections
Huy V. Vo, Patrick Pérez, and Jean Ponce · 2020
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Relation networks for object detection
Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, and Yichen Wei · 2018
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Pcl: Proposal cluster learning for weakly supervised object detection
Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, and Alan Yuille · 2018
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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Active learning for deep detection neural networks
Hamed H. Aghdam, Abel Gonzalez-Garcia, Joost van de Weijer, and Antonio M. López · 2019
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Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 2019
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Object discovery from a single unlabeled image by mining frequent itemsets with multi-scale features
Runsheng Zhang, Yaping Huang, Mengyang Pu, Jian Zhang, Qingji Guan, Qi Zou, and Haibin Ling · 2020
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Self-supervised object detection from audio-visual correspondence
Triantafyllos Afouras, Yuki M Asano, Francois Fagan, Andrea Vedaldi, and Florian Metze · 2021
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Understanding robustness of transformers for image classification
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Emerging properties in self-supervised vision transformers
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Unbiased teacher for semi-supervised object detection
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Intriguing properties of vision transformers
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Proposal learning for semi-supervised object detection
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