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This paper starts by revealing a surprising finding: without any learning, a randomly initialized CNN can localize objects surprisingly well.
Improved baselines with momentum contrastive learning
Chen, X.; Fan, H.; Girshick, R.; and He, K. 2020b · 2003
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Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning
Shen, Z.; Liu, Z.; Liu, Z.; Savvides, M.; Darrell, T.; and Xing, E. 2020 · 2003
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The PASCAL visual object classes (VOC) challenge
Everingham, M.; Gool, L. V.; Williams, C. K.; Winn, J.; and Zisserman, A. 2010 · 2010
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Understanding the difficulty of training deep feedforward neural networks
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Beyond Single Instance Multi-view Unsupervised Representation Learning
Chu, X.; Zhan, X.; and Wei, X. 2020 · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
Wah, C.; Branson, S.; Welinder, P.; Perona, P.; and Belongie, S. 2011 · 2011
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Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Ghiasi, G.; Cui, Y.; Srinivas, A.; Qian, R.; Lin, T.-Y.; D.Cubuk, E.; Le, Q. V.; and Zoph, B. 2012 · 2012
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Microsoft COCO: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Edge Boxes: Locating Object Proposals from Edges
Zitnick, C. L.; and Dollár, P. 2014 · 2014
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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ImageNet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015 · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Rethinking the Inception Architecture for Computer Vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2016 · 2016
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Learning deep features for discriminative localization
Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; and Torralba, A. 2016 · 2016
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Inductive bias of deep convolutional networks through pooling geometry
Cohen, N.; and Shashua, A. 2017 · 2017
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Mask R-CNN
He, K.; Gkioxari, G.; Dollár, P.; and Girshick, R. 2017 · 2017
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transformation
Wei, X.-S.; Zhang, C.-L.; Wu, J.; Shen, C.; and Zhou, Z.-H. 2019 · 2019
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Detectron2
Wu, Y.; Kirillov, A.; Massa, F.; Lo, W.-Y.; and Girshick, R. 2019 · 2019
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CutMix: Regularization strategy to train strong classifiers with localizable features
Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. 2019 · 2019
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Boostrap your own latent: A new approach to self-supervised learning
Grill, J.-B.; Strub, F.; Altche, F.; Tallec, C.; H.Richemond, P.; Buchatskaya, E.; Doersch, C.; Pires, B. A.; Guo, Z. D.; Azar, M. G.; Piot, B.; Kavukcuoglu, K.; Munos, R.; and Valko, M. 2020 · 2020
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Feature pyramid networks for object detection
Lin, T.-Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; and Belongie, S. 2017 · 2017
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; and Batra, D. 2017 · 2017
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Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Wei, X.-S.; Luo, J.-H.; Wu, J.; and Zhou, Z.-H. 2017 · 2017
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Deep Image Prior
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2018 · 2018
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Representation learning with contrastive predictive coding
van den Oord, A.; Li, Y.; and Vinyals, O. 2018 · 2018
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Mixup: Beyond empirical risk minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2018 · 2018
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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Supervised Contrastive Learning
Khosla, P.; Teterwak, P.; Wang, C.; Sarna, A.; Tian, Y.; Isola, P.; Maschinot, A.; Liu, C.; and Krishnan, D. 2020 · 2020
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Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Malach, E.; Yehudai, G.; Shalev-Schwartz, S.; and Shamir, O. 2020 · 2020
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Rethinking the Route Towards Weakly Supervised Object Localization
Zhang, C.-L.; Cao, Y.-H.; and Wu, J. 2020 · 2020
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Rethinking self-supervised learning: Small is beautiful
Cao, Y.-H.; and Wu, J. 2021 · 2021
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The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
Chen, T.; Frankle, J.; Chang, S.; Liu, S.; Zhang, Y.; Carbin, M.; and Wang, Z. 2021 · 2021
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; and Houlsby, N. 2021 · 2021
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The Lottery Ticket Hypothesis for Object Recognition
Girish, S.; Maiya, S. R.; Gupta, K.; Chen, H.; Davis, L.; and Shrivastava, A. 2021 · 2021
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