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Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Toward open set recognition
Walter J Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E Boult · 2012
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Selective search for object recognition
Jasper RR Uijlings, Koen EA Van De Sande, Theo Gevers, and Arnold WM Smeulders · 2013
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Multi-class open set recognition using probability of inclusion
Lalit P Jain, Walter J Scheirer, and Terrance E Boult · 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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Probability models for open set recognition
Walter J Scheirer, Lalit P Jain, and Terrance E Boult · 2014
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Towards open world recognition
Abhijit Bendale and Terrance Boult · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
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Dropout sampling for robust object detection in open-set conditions
Dimity Miller, Lachlan Nicholson, Feras Dayoub, and Niko Sünderhauf · 2018
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Open-world learning and application to product classification
Hu Xu, Bing Liu, Lei Shu, and P Yu · 2019
Overcoming language priors in vqa via decomposed linguistic representations
Chenchen Jing, Yuwei Wu, Xiaoxun Zhang, Yunde Jia, and Qi Wu · 2020
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A peek into the reasoning of neural networks: Interpreting with structural visual concepts
Yunhao Ge, Yao Xiao, Zhi Xu, Meng Zheng, Srikrishna Karanam, Terrence Chen, Laurent Itti, and Ziyan Wu · 2021
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Transformation driven visual reasoning
Xin Hong, Yanyan Lan, Liang Pang, Jiafeng Guo, and Xueqi Cheng · 2021
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Towards open world object detection
KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian · 2021
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Counterfactual vqa: A cause-effect look at language bias
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen · 2021
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Categorical depth distribution network for monocular 3d object detection
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D2det: Towards high quality object detection and instance segmentation
Jiale Cao, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Yanwei Pang, and Ling Shao · 2020
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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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Cody Reading, Ali Harakeh, Julia Chae, and Steven L Waslander · 2021
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Counterfactual zero-shot and open-set visual recognition
Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, and Hanwang Zhang · 2021
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Semantic relation reasoning for shot-stable few-shot object detection
Chenchen Zhu, Fangyi Chen, Uzair Ahmed, Zhiqiang Shen, and Marios Savvides · 2021
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