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Object segmentation requires both object-level information and low-level pixel data.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Semantic texton forests for image categorization and segmentation
Shotton, J., Johnson, M., Cipolla, R.: · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: · 2009
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Object detection with discriminatively trained part-based models
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: · 2010
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The PASCAL visual object classes (VOC) challenge
Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: · 2010
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Deconvolutional networks
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.: · 2012
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Measuring the objectness of image windows
Alexe, B., Deselaers, T., Ferrari, V.: · 2012
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Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 2013
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Pedestrian detection with unsupervised multi-stage feature learning
Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: · 2013
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Selective search for object recog
Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.: · 2013
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Overfeat: Integrated recognition, localization and detection using conv nets
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 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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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
Cited alongside, same era.
Recurrent conv. neural networks for scene labeling
Pinheiro, P.O., Collobert, R.: · 2014
Cited alongside, same era.
Simultaneous detection and segmentation
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2014
Cited alongside, same era.
Edge boxes: Locating object proposals from edges
Zitnick, C.L., Dollár, P.: · 2014
Cited alongside, same era.
Geodesic object proposals
Krähenbühl, P., Koltun, V.: · 2014
Cited alongside, same era.
RIGOR: Reusing Inference in Graph Cuts for generating Object Regions
Humayun, A., Li, F., Rehg, J.M.: · 2014
Cited alongside, same era.
Learning to segment object candidates
Pinheiro, P.O., Collobert, R., Dollár, P.: · 2015
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Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 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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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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Girshick, R.: · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
Cited alongside, same era.
Microsoft COCO: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., Dollár, P.: · 2015
Cited alongside, same era.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
Eigen, D., Fergus, R.: · 2015
Cited alongside, same era.
Conditional random fields as recurrent neural nets
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, B., Su, Z., Du, D., Huang, C., Torr, P.: · 2015
Cited alongside, same era.
Semantic image segmentation with deep conv. nets and fully connected CRFs
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2015
Cited alongside, same era.
Multiscale combinatorial grouping for image segmentation and object proposal gen
Pont-Tuset, J., Arbeláez, P., Barron, J., Marques, F., Malik, J.: · 2015
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What makes for effective detection proposals?
Hosang, J., Benenson, R., Dollár, P., Schiele, B.: · 2015
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Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., v.d. Smagt, P., Cremers, D., Brox, T.: · 2015
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural nets
Bell, S., Zitnick, C.L., Bala, K., Girshick, R.: · 2016
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Instance-aware semantic segmentation via multi-task network cascades
Dai, J., He, K., Sun, J.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 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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