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Knowledge distillation has shown great success in classification, however, it is still challenging for detection.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
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Microsoft coco: Common objects in context
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VQA: visual question answering
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The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
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Learning both weights and connections for efficient neural networks
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Faster R-CNN: towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 2015
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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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
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Binarized neural networks
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Xnor-net: Imagenet classification using binary convolutional neural networks
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Learning efficient object detection models with knowledge distillation
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Mask R-CNN
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Channel pruning for accelerating very deep neural networks
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Quantized neural networks: Training neural networks with low precision weights and activations
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Mimicking very efficient network for object detection
Quanquan Li, Shengying Jin, and Junjie Yan · 2017
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Feature pyramid networks for object detection
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Focal loss for dense object detection
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Attention is all you need
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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, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
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AdelaiDet: A toolbox for instance-level recognition tasks
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FCOS: fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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Distilling object detectors with fine-grained feature imitation
Tao Wang, Li Yuan, Xiaopeng Zhang, and Jiashi Feng · 2019
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A faster pytorch implementation of faster r-cnn
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
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Bottom-up and top-down attention for image captioning and visual question answering
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Cascade r-cnn: Delving into high quality object detection
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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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Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
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Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Explaining knowledge distillation by quantifying the knowledge
Xu Cheng, Zhefan Rao, Yilan Chen, and Quanshi Zhang · 2020
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Conditional convolutions for instance segmentation
Zhi Tian, Chunhua Shen, and Hao Chen · 2020
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SOLOv2: Dynamic and fast instance segmentation
Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, and Chunhua Shen · 2020
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Revisiting knowledge distillation via label smoothing regularization
Li Yuan, Francis E. H. Tay, Guilin Li, Tao Wang, and Jiashi Feng · 2020
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Points as queries: Weakly semi-supervised object detection by points
Liangyu Chen, Tong Yang, Xiangyu Zhang, Wei Zhang, and Jian Sun · 2021
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General instance distillation for object detection
Xing Dai, Zeren Jiang, Zhao Wu, Yiping Bao, Zhicheng Wang, Si Liu, and Erjin Zhou · 2021
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Knowledge distillation: A survey
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Distilling object detectors via decoupled features
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End-to-end object detection with fully convolutional network
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Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors
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