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A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection.
Data-driven 3d voxel patterns for object category recognition
Xiang, Y., Choi, W., Lin, Y., Savarese, S.: · 1911
Earlier work this paper cites.
Deep learning strong parts for pedestrian detection
Tian, Y., Luo, P., Wang, X., Tang, X.: · 1912
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Robust real-time face detection
Viola, P.A., Jones, M.J.: · 2004
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Robust object detection via soft cascade
Bourdev, L.D., Brandt, J.: · 2005
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Object detection with discriminatively trained part-based models
Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: · 2010
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The pascal visual object classes (VOC) challenge
Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: · 2010
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Segmentation as selective search for object recognition
van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: · 2011
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Joint 3d estimation of objects and scene layout
Geiger, A., Wojek, C., Urtasun, R.: · 2011
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Are we ready for autonomous driving? the KITTI vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
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Pedestrian detection: An evaluation of the state of the art
Dollár, P., Wojek, C., Schiele, B., Perona, P.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Pedestrian detection at 100 frames per second
Benenson, R., Mathias, M., Timofte, R., Gool, L.J.V.: · 2012
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Regionlets for generic object detection
Wang, X., Yang, M., Zhu, S., Lin, Y.: · 2013
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Fast feature pyramids for object detection
Dollár, P., Appel, R., Belongie, S.J., Perona, P.: · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
Cited alongside, same era.
Boosting algorithms for detector cascade learning
Saberian, M.J., Vasconcelos, N.: · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
BING: binarized normed gradients for objectness estimation at 300fps
Cheng, M., Zhang, Z., Lin, W., Torr, P.H.S.: · 2014
Cited alongside, same era.
Edge boxes: Locating object proposals from edges
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Holistically-nested edge detection
Xie, S., Tu, Z.: · 2015
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Learning complexity-aware cascades for deep pedestrian detection
Cai, Z., Saberian, M.J., Vasconcelos, N.: · 2015
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Learning to detect vehicles by clustering appearance patterns
Ohn-Bar, E., Trivedi, M.M.: · 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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Deeply-supervised nets
Lee, C., Xie, S., Gallagher, P.W., Zhang, Z., Tu, Z.: · 2015
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Zitnick, C.L., Dollár, P.: · 2014
Cited alongside, same era.
Multiscale combinatorial grouping
Arbeláez, P.A., Pont-Tuset, J., Barron, J.T., Marqués, F., Malik, J.: · 2014
Cited alongside, same era.
Integrating context and occlusion for car detection by hierarchical and-or model
Li, B., Wu, T., Zhu, S.: · 2014
Cited alongside, same era.
Pedestrian detection with spatially pooled features and structured ensemble learning
Paisitkriangkrai, S., Shen, C., van den Hengel, A.: · 2014
Cited alongside, same era.
Local decorrelation for improved pedestrian detection
Nam, W., Dollár, P., Han, J.H.: · 2014
Cited alongside, same era.
Fast R-CNN
Girshick, R.B.: · 2015
Cited alongside, same era.
3d object proposals for accurate object class detection
Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., Urtasun, R.: · 2015
Cited alongside, same era.
segdeepm: Exploiting segmentation and context in deep neural networks for object detection
Zhu, Y., Urtasun, R., Salakhutdinov, R., Fidler, S.: · 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.S., Berg, A.C., Li, F.: · 2015
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What makes for effective detection proposals?
Hosang, J., Benenson, R., Dollár, P., Schiele, B.: · 2015
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Multi-view and 3d deformable part models
Pepik, B., Stark, M., Gehler, P.V., Schiele, B.: · 2015
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Filtered channel features for pedestrian detection
Zhang, S., Benenson, R., Schiele, B.: · 2015
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: · 2016
Closest in time.
Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Bell, S., Zitnick, C.L., Bala, K., Girshick, R.B.: · 2016
Closest in time.
Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers
Yang, F., Choi, W., Lin, Y.: · 2016
Closest in time.