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Recent CNN based object detectors, no matter one-stage methods like YOLO, SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are usually trying to directly finetune from ImageNet pre-trained models designed for image classification.
A wavelet tour of signal processing
Mallat, S.: · 1999
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Distinctive image features from scale-invariant keypoints
Lowe, D.G.: · 2004
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Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Discriminatively trained deformable part models, release 5
Girshick, R.B., Felzenszwalb, P.F., McAllester, D.: · 2012
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Selective search for object recognition
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Edge boxes: Locating object proposals from edges
Zitnick, C.L., Dollár, P.: · 2014
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 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., Zitnick, C.L.: · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 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., et al.: · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: · 2016
Cited alongside, same era.
Yolo9000: Better, faster, stronger
Redmon, J., Farhadi, A.: · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
Li, Y., He, K., Sun, J., et al.: · 2016
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: · 2017
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2017
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Squeeze-and-excitation networks
Hu, J., Shen, L., Sun, G.: · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., Sun, J.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
Chollet, F.: · 2016
Cited alongside, same era.
Ms coco api. https://github.com/pdollar/coco
Lin, T.Y., Dollár, P.: · 2016
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al.: · 2016
Cited alongside, same era.
Instance-aware semantic segmentation via multi-task network cascades
Dai, J., He, K., Sun, J.: · 2016
Cited alongside, same era.
Later among the works it cites.
Dilated residual networks
Yu, F., Koltun, V., Funkhouser, T.: · 2017
Later among the works it cites.
Deformable convolutional networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: · 2017
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Dsod: Learning deeply supervised object detectors from scratch
Shen, Z., Liu, Z., Li, J., Jiang, Y.G., Chen, Y., Xue, X.: · 2017
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He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
Later among the works it cites.
Megdet: A large mini-batch object detector
Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., Sun, J.: · 2017
Later among the works it cites.
Dssd: Deconvolutional single shot detector
Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C.: · 2017
Later among the works it cites.
Fully convolutional instance-aware semantic segmentation
Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: · 2017
Later among the works it cites.
Detectron
Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.: · 2018
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