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We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks.
Probability of error of some adaptive pattern-recognition machines
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Finding pictures of objects in large collections of images
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On seeing stuff: the perception of materials by humans and machines
Adelson, E.H.: · 2001
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Grabcut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., Blake, A.: · 2004
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Scene segmentation with crfs learned from partially labeled images
Verbeek, J.J., Triggs, B.: · 2008
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Learning hybrid models for image annotation with partially labeled data
He, X., Zemel, R.S.: · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2010
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Efficient inference in fully connected CRFs with Gaussian edge potentials
Krähenbühl, P., Koltun, V.: · 2011
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Semantic contours from inverse detectors
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: · 2011
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Layered object models for image segmentation
Yang, Y., Hallman, S., Ramanan, D., Fowlkes, C.C.: · 2012
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Saliency detection via graph-based manifold ranking
Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: · 2013
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Training object class detectors from eye tracking data
Papadopoulos, D.P., Clarke, A.D., Keller, F., Ferrari, V.: · 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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Simultaneous detection and segmentation
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2014
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Multiscale combinatorial grouping
Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 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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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
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Weakly- and semi-supervised learning of a DCNN for semantic image segmentation
Papandreou, G., Chen, L., Murphy, K., Yuille, A.L.: · 2015
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Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Dai, J., He, K., Sun, J.: · 2015
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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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Global contrast based salient region detection
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: · 2015
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From image-level to pixel-level labeling with convolutional networks
Pinheiro, P.O., Collobert, R.: · 2015
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Constrained convolutional neural networks for weakly supervised segmentation
Pathak, D., Krahenbuhl, P., Darrell, T.: · 2015
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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.: · 2015
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Conditional random fields as recurrent neural networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.: · 2015
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Multi-instance object segmentation with occlusion handling
Chen, Y.T., Liu, X., Yang, M.H.: · 2015
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Proposal-free network for instance-level object segmentation
Liang, X., Wei, Y., Shen, X., Yang, J., Lin, L., Yan, S.: · 2015
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Pixelwise instance segmentation with a dynamically instantiated network
Arnab, A., Torr, P.H.S.: · 2017
Later among the works it cites.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
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Fully convolutional instance-aware semantic segmentation
Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: · 2017
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Deep watershed transform for instance segmentation
Bai, M., Urtasun, R.: · 2017
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Semantic instance segmentation with a discriminative loss function
De Brabandere, B., Neven, D., Van Gool, L.: · 2017
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Instancecut: from edges to instances with multicut
Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., Rother, C.: · 2017
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Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
Cited alongside, same era.
Seed, expand and constrain: Three principles for weakly-supervised image segmentation
Kolesnikov, A., Lampert, C.H.: · 2016
Cited alongside, same era.
What’s the point: Semantic segmentation with point supervision
Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: · 2016
Cited alongside, same era.
Bottom-up instance segmentation using deep higher-order crfs
Arnab, A., Torr, P.H.S.: · 2016
Cited alongside, same era.
Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
Lin, D., Dai, J., Jia, J., He, K., Sun, J.: · 2016
Cited alongside, same era.
Sgn: Sequential grouping networks for instance segmentation
Liu, S., Jia, J., Fidler, S., Urtasun, R.: · 2017
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Stc: A simple to complex framework for weakly-supervised semantic segmentation
Wei, Y., Liang, X., Chen, Y., Shen, X., Cheng, M.M., Feng, J., Zhao, Y., Yan, S.: · 2017
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Exploiting saliency for object segmentation from image level labels
Oh, S.J., Benenson, R., Khoreva, A., Akata, Z., Fritz, M., Schiele, B.: · 2017
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Discovering class-specific pixels for weakly-supervised semantic segmentation
Chaudhry, A., Dokania, P.K., Torr, P.H.: · 2017
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Simple does it: Weakly supervised instance and semantic segmentation
Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B.: · 2017
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Learning to segment every thing
Hu, R., Dollár, P., He, K., Darrell, T., Girshick, R.: · 2017
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Pixelnet: Representation of the pixels, by the pixels, and for the pixels
Bansal, A., Chen, X., Russell, B., Gupta, A., Ramanan, D.: · 2017
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Full-resolution residual networks for semantic segmentation in street scenes
Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: · 2017
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Holistic, instance-level human parsing
Li, Q., Arnab, A., Torr, P.H.: · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: · 2017
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Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
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Rethinking atrous convolution for semantic image segmentation
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: · 2017
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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.: · 2017
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End-to-end instance segmentation with recurrent attention
Ren, M., Zemel, R.S.: · 2017
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Kirillov, A., He, K., Girshick, R., Rother, C., Dollár, P.: · 2018
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Path aggregation network for instance segmentation
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: · 2018
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Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction
Arnab, A., Zheng, S., Jayasumana, S., Romera-Paredes, B., Larsson, M., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., Torr, P.H.S.: · 2018
Closest in time.