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Object detection and instance segmentation are dominated by region-based methods such as Mask RCNN.
Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15
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Ballard, D.H.: Readings in computer vision: Issues, problems, principles, and paradigms. chap. Generalizing the Hough Transform to Detect Arbitrary Shapes, pp. 714–725. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1987)
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Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22
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Leibe, B., Schiele, B.: Interleaved object categorization and segmentation. In: Proc. BMVC (2003)
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Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59
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Ladický, L., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.: What, where and how many? combining object detectors and crfs. In: Proc. ECCV (2010)
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Wählby, C., Riklin-Raviv, T., Ljosa, V., Conery, A.L., Golland, P., Ausubel, F.M., Carpenter, A.E.: Resolving clustered worms via probabilistic shape models. In: Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. pp. 552–555. IEEE (2010)
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Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
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Ljosa, V., Sokolnicki, K.L., Carpenter, A.E.: Annotated high-throughput microscopy image sets for validation. Nat Methods 9
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Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proc. CVPR (2014)
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Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Proc. ECCV (2014)
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
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Silberman, N., Sontag, D., Fergus, R.: Instance segmentation of indoor scenes using a coverage loss. In: Proc. ECCV (2014)
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Tighe, J., Niethammer, M., Lazebnik, S.: Scene parsing with object instances and occlusion ordering. In: Proc. CVPR (2014)
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Chen, Y.T., Liu, X., Yang, M.H.: Multi-instance object segmentation with occlusion handling. In: Proc. CVPR (2015)
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Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation. In: Proc. CVPR (2015)
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Feragen, A., Lauze, F., Hauberg, S.: Geodesic exponential kernels: When curvature and linearity conflict. In: Proc. CVPR (2015)
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Girshick, R.: Fast r-cnn. In: Proc. ICCV (2015)
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2015
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Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proc. CVPR (2015)
2015
Cited alongside, same era.
Pinheiro, P.O., Collobert, R., Dollár, P.: Learning to segment object candidates. In: Proc. NIPS (2015)
2015
Cited alongside, same era.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: Proc. NIPS (2015)
2015
Cited alongside, same era.
Wang, L., Lu, H., Ruan, X., Yang, M.H.: Deep networks for saliency detection via local estimation and global search. In: Proc. CVPR (June 2015)
2015
Cited alongside, same era.
Zhang, Z., Schwing, A.G., Fidler, S., Urtasun, R.: Monocular object instance segmentation and depth ordering with cnns. In: Proc. ICCV (2015)
2015
Cited alongside, same era.
Chandra, S., Usunier, N., Kokkinos, I.: Dense and Low-Rank Gaussian CRFs Using Deep Embeddings. In: Proc. ICCV (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
Hayder, Z., He, X., Salzmann, M.: Boundary-aware instance segmentation. In: Proc. CVPR (2017)
2017
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He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proc. ICCV (2017)
2017
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Andriluka, M., Stewart, R., Ng, A.Y.: End-to-end people detection in crowded scenes. In: Proc. CVPR (2016)
2016
Cited alongside, same era.
Dai, J., He, K., Li, Y., Ren, S., Sun, J.: Instance-sensitive fully convolutional networks. In: Proc. ECCV (2016)
2016
Cited alongside, same era.
Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: Proc. CVPR (2016)
2016
Cited alongside, same era.
Dai, J., Li, Y., He, K., Sun, J.: R-fcn: Object detection via region-based fully convolutional networks. In: Proc. NIPS, pp. 379–387 (2016)
2016
Cited alongside, same era.
Harley, A.W., Derpanis, K.G., Kokkinos, I.: Learning dense convolutional embeddings for semantic segmentation. In: Proc. ICLR (2016)
2016
Cited alongside, same era.
Liang, X., Wei, Y., Shen, X., Jie, Z., Feng, J., Lin, L., Yan, S.: Reversible recursive instance-level object segmentation. In: Proc. CVPR (2016)
2016
Cited alongside, same era.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Proc. ECCV (2016)
2016
Cited alongside, same era.
Hu, H., Lan, S., Jiang, Y., Cao, Z., Sha, F.: Fastmask: Segment multi-scale object candidates in one shot. In: Proc. CVPR (2017)
2017
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Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. Proc. CVPR (2017)
2017
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Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., Rother, C.: Instancecut: From edges to instances with multicut. In: Proc. CVPR (July 2017)
2017
Later among the works it cites.
Kokkinos, I.: Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: Proc. CVPR (2017)
2017
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Kong, S., Fowlkes, C.: Recurrent pixel embedding for instance grouping. arXiv (2017)
2017
Later among the works it cites.
Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. In: Proc. CVPR (2017)
2017
Later among the works it cites.
Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proc. CVPR (2017)
2017
Later among the works it cites.
Liu, S., Jia, J., Fidler, S., Urtasun, R.: Sgn: Sequential grouping networks for instance segmentation. In: Proc. ICCV (2017)
2017
Later among the works it cites.
Newell, A., Huang, Z., Deng, J.: Associative embedding: End-to-end learning for joint detection and grouping. In: Proc. NIPS (2017)
2017
Later among the works it cites.
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proc. CVPR (2017)
2017
Later among the works it cites.
Ren, M., Zemel, R.S.: End-to-end instance segmentation with recurrent attention. In: Proc. CVPR (2017)
2017
Later among the works it cites.
Kong, S., Fowlkes, C.: Recurrent pixel embedding for instance grouping. In: Proc. CVPR (2018)
2018
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