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The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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The pascal visual object classes (voc) challenge
Everingham, M., Gool, L.V., I.Williams, C.K., J.Winn, Zisserman, A.: · 2010
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Understanding the difficulty of training deep feedforward neuralnetworks
Bengio, Y., Glorot, X.: · 2010
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Segmentation as selective search for object recognition
Smeulders, A., Gevers, T., Sebe, N., Snoek, C.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.: · 2012
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Imagenet large scale visual recognition challenge (2014)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, 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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Edge boxes: Locating object proposals from edges
Zitnick, C.L., Dollár, P.: · 2014
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Scalable, high-quality object detection
Szegedy, C., Reed, S., Erhan, D., Anguelov, D.: · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
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Multiscale combinatorial grouping
Arbeláez, P., Pont-Tuset, J., Barron, J.T., Marques, F., Malik, J.: · 2014
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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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Fast r-cnn
Girshick, R.: · 2015
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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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Boosting object proposals: From pascal to coco
Pont-Tuset, J., Van Gool, L.: · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2015
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Craft objects from images
Yang, B., Yan, J., Lei, Z., Li, S.Z.: · 2016
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Locnet: Improving localization accuracy for object detection
Gidaris, S., Komodakis, N.: · 2016
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Attend refine repeat: Active box proposal generation via in-out localization
Gidaris, S., Komodakis, N.: · 2016
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Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.: · 2016
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A multipath network for object detection
Zagoruyko, S., Lerer, A., Lin, T.Y., Pinheiro, P.O., Gross, S., Chintala, S., Dollár, P.: · 2016
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Object detection via a multi-region and semantic segmentation-aware cnn model
Gidaris, S., Komodakis, N.: · 2015
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., Farhadi, 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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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Deepid-net: Deformable deep convolutional neural networks for object detection
Ouyang, W., Wang, X., Zeng, X., Qiu, S., Luo, P., Tian, Y., Li, H., Yang, S., Wang, Z., Loy, C.C., et al.: · 2015
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Deformable part models are convolutional neural networks
Girshick, R., Iandola, F., Darrell, T., Malik, J.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Multi-bias non-linear activation in deep neural networks
Li, H., Ouyang, W., Wang, X.: · 2016
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Gated bi-directional cnn for object detection
Zeng, X., Ouyang, W., Yang, B., Yan, J., Wang, X.: · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.: · 2016
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What makes for effective detection proposals?
Hosang, J., Benenson, R., Dollár, P., Schiele, B.: · 2016
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