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Object detection has recently achieved a breakthrough for removing the last one non-differentiable component in the pipeline, Non-Maximum Suppression (NMS), and building up an end-to-end system.
The Hungarian method for the assignment problem
Kuhn, H. W · 1955
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Perceptron algorithms for the classification of non-separable populations
Burton, R. M., Herold G, D., and Rienk S, V · 1997
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Rapid object detection using a boosted cascade of simple features
Viola, P. and Jones, M · 2001
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Histograms of oriented gradients for human detection
Dalal, N. and Triggs, B · 2005
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The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2010
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Object detection with discriminatively trained part based models
Felzenszwalb, P., Girshick, R., McAllester, D., and Ramanan, D · 2010
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Sparse r-cnn: End-to-end object detection with learnable proposals
Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., Wang, C., et al · 2011
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Rethinking transformer-based set prediction for object detection
Sun, Z., Cao, S., Yang, Y., and Kitani, K · 2011
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Selective search for object recognition
Uijlings, J. R., Van De Sande, K. E., Gevers, T., and Smeulders, A. W · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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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
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Fast R-CNN
Girshick, R · 2015
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DenseBox: Unifying landmark localization with end to end object detection
Huang, L., Yang, Y., Deng, Y., and Yu, Y · 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
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Cited alongside, same era.
R-FCN: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., and Sun, J · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
SSD: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
Cited alongside, same era.
Crowdhuman: A benchmark for detecting human in a crowd
Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., and Sun, J · 2018
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Grid R-CNN
Lu, X., Li, B., Yue, Y., Li, Q., and Yan, J · 2019
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Libra R-CNN: Towards balanced learning for object detection
Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D · 2019
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Generalized intersection over union: A metric and a loss for bounding box regression
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S · 2019
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FCOS: Fully convolutional one-stage object detection
Tian, Z., Shen, C., Chen, H., and He, T · 2019
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Cascade RPN: Delving into high-quality region proposal network with adaptive convolution
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Mask R-CNN
He, K., Gkioxari, G., Dollar, P., and Girshick, R · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Associative embedding: End-to-end learning for joint detection and grouping
Newell, A., Huang, Z., and Deng, J · 2017
Cited alongside, same era.
YOLO9000: Better, faster, stronger
Redmon, J. and Farhadi, A · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Cascade R-CNN: Delving into high quality object detection
Cai, Z. and Vasconcelos, N · 2018
Cited alongside, same era.
Vu, T., Jang, H., Pham, T. X., and Yoo, C. D · 2019
Later among the works it cites.
Double anchor r-cnn for human detection in a crowd
Zhang, K., Xiong, F., Sun, P., Hu, L., Li, B., and Yu, G · 2019
Later among the works it cites.
Zhou, X., Wang, D., and Krähenbühl, P · 2019
Later among the works it cites.
End-to-End object detection with transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S · 2020
Closest in time.
Up-detr: Unsupervised pre-training for object detection with transformers
Dai, Z., Cai, B., Lin, Y., and Chen, J · 2020
Closest in time.
End-to-end object detection with fully convolutional network
Wang, J., Song, L., Li, Z., Sun, H., Sun, J., and Zheng, N · 2020
Closest in time.
End-to-end object detection with adaptive clustering transformer
Zheng, M., Gao, P., Wang, X., Li, H., and Dong, H · 2020
Closest in time.
Deformable detr: Deformable transformers for end-to-end object detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J · 2020
Closest in time.
Fast convergence of detr with spatially modulated co-attention
Gao, P., Zheng, M., Wang, X., Dai, J., and Li, H · 2021
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