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Object occlusion boundary detection is a fundamental and crucial research problem in computer vision.
Machine perception of three-dimensional solids
Roberts, L.G.: · 1963
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The 2.1-d sketch
Nitzberg, M., Mumford, D.: · 1990
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Interpreting line drawings of curved objects with tangential edges and surfaces
Cooper, M.C.: · 1997
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Learning to detect natural image boundaries using local brightness, color, and texture cues
Martin, D.R., Fowlkes, C.C., Malik, J.: · 2004
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Figure/ground assignment in natural images
Ren, X., Fowlkes, C.C., Malik, J.: · 2006
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Recovering occlusion boundaries from a single image
Hoiem, D., Stein, A.N., Efros, A.A., Hebert, M.: · 2007
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Boundary ownership by lifting to 2.1 d
Leichter, I., Lindenbaum, M.: · 2009
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Occlusion boundary detection using pseudo-depth
He, X., Yuille, A.: · 2010
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Simultaneous segmentation and figure/ground organization using angular embedding
Maire, M.: · 2010
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A segmentation-aware object detection model with occlusion handling
Gao, T., Packer, B., Koller, D.: · 2011
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Occlusion boundary detection and figure/ground assignment from optical flow
Sundberg, P., Brox, T., Maire, M., Arbeláez, P., Malik, J.: · 2011
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Scene parsing with object instances and occlusion ordering
Tighe, J., Niethammer, M., Lazebnik, S.: · 2014
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Occluding contours for multi-view stereo
Shan, Q., Curless, B., Furukawa, Y., Hernandez, C., Seitz, S.M.: · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Monocular object instance segmentation and depth ordering with cnns
Zhang, Z., Schwing, A.G., Fidler, S., Urtasun, R.: · 2015
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Holistically-nested edge detection
Xie, S., Tu, Z.: · 2015
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Pushing the boundaries of boundary detection using deep learning
Kokkinos, I.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
Doc: Deep occlusion estimation from a single image
Wang, P., Yuille, A.: · 2016
Later among the works it cites.
Occlusion boundary detection via deep exploration of context
Fu, H., Wang, C., Tao, D., Black, M.J.: · 2016
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Object contour detection with a fully convolutional encoder-decoder network
Yang, J., Price, B., Cohen, S., Lee, H., Yang, M.H.: · 2016
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Learning relaxed deep supervision for better edge detection
Liu, Y., Lew, M.S.: · 2016
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Affinity cnn: Learning pixel-centric pairwise relations for figure/ground embedding
Maire, M., Narihira, T., Yu, S.X.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
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Multi-scale context aggregation by dilated convolutions
Yu, F., Koltun, V.: · 2015
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Fast 2d border ownership assignment
Teo, C.L., Fermüller, C., Aloimonos, Y.: · 2015
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Fast edge detection using structured forests
Dollár, P., Zitnick, C.L.: · 2015
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Pixel-wise deep learning for contour detection
Hwang, J.J., Liu, T.L.: · 2015
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Fast r-cnn
Girshick, R.: · 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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Richer convolutional features for edge detection
Liu, Y., Cheng, M.M., Hu, X., Wang, K., Bai, X.: · 2017
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Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: · 2017
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., Cipolla, R.: · 2017
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Dilated residual networks
Yu, F., Koltun, V., Funkhouser, T.: · 2017
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Learning hybrid convolutional features for edge detection
Hu, X., Liu, Y., Wang, K., Ren, B.: · 2018
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Semantic edge detection with diverse deep supervision
Liu, Y., Cheng, M.M., Bian, J., Zhang, L., Jiang, P.T., Cao, Y.: · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2018
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