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Object detection methods have witnessed impressive improvements in the last years thanks to the design of novel neural network architectures and the availability of large scale datasets.
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.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2009
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 C Lawrence Zitnick · 2014
Earlier work this paper cites.
Fast r-cnn
Ross Girshick · 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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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Cited alongside, same era.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
Cited alongside, same era.
Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
Cited alongside, same era.
Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance Boult · 2018
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2018
Addressing failure prediction by learning model confidence
Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, and Patrick Pérez · 2019
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Learning rich features at high-speed for single-shot object detection
Tiancai Wang, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, and Ling Shao · 2019
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The overlooked elephant of object detection: Open set
Akshay Dhamija, Manuel Gunther, Jonathan Ventura, and Terrance Boult · 2020
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Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2020
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Background data resampling for outlier-aware classification
Yi Li and Nuno Vasconcelos · 2020
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Energy-based out-of-distribution detection
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Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2018
Cited alongside, same era.
Dropout sampling for robust object detection in open-set conditions
Dimity Miller, Lachlan Nicholson, Feras Dayoub, and Niko Sünderhauf · 2018
Cited alongside, same era.
Out-of-distribution detection using an ensemble of self supervised leave-out classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, and Theodore L Willke · 2018
Cited alongside, same era.
Hierarchical shot detector
Jiale Cao, Yanwei Pang, Jungong Han, and Xuelong Li · 2019
Cited alongside, same era.
Towards open world recognition
Abhijit Bendale and Terrance Boult
Cited in the paper.
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
Later among the works it cites.
Faster ilod: Incremental learning for object detectors based on faster rcnn
Can Peng, Kun Zhao, and Brian C Lovell · 2020
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Generative-discriminative feature representations for open-set recognition
Pramuditha Perera, Vlad I Morariu, Rajiv Jain, Varun Manjunatha, Curtis Wigington, Vicente Ordonez, and Vishal M Patel · 2020
Later among the works it cites.
Ow-detr: Open-world detection transformer
Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah · 2021
Later among the works it cites.
Towards open world object detection
KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian · 2021
Later among the works it cites.
Learning placeholders for open-set recognition
Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2021
Later among the works it cites.