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Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image.
Human malaria parasites in continuous culture
Trager, W., Jensen, J.B.: · 1976
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Parametric correspondence and chamfer matching: Two new techniques for image matching
Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: · 1977
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Dissociable mechanisms of subitizing and counting: neuropsychological evidence from simultanagnosic patients
Dehaene, S., Cohen, L.: · 1994
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Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Greedy learning of multiple objects in images using robust statistics and factorial learning
Williams, C.K., Titsias, M.K.: · 2004
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Effort during visual search and counting: Insights from pupillometry
Porter, G., Troscianko, T., Gilchrist, I.D.: · 2007
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Offline handwriting recognition with multidimensional recurrent neural networks
Graves, A., Schmidhuber, J.: · 2009
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What, where and how many? combining object detectors and crfs
Ladickỳ, L., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.: · 2010
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Human instance segmentation from video using detector-based conditional random fields
Vineet, V., Warrell, J., Ladicky, L., Torr, P.H.: · 2011
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Torch7: A matlab-like environment for machine learning
Collobert, R., Kavukcuoglu, K., Farabet, C.: · 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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Learning to detect partially overlapping instances
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: · 2013
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Parameter learning and convergent inference for dense random fields
Krähenbühl, P., Koltun, V.: · 2013
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Simultaneous detection and segmentation
Hariharan, B., Arbeláez, P., Girshick, R., Malik, 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
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Multiscale combinatorial grouping
Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: · 2014
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Scene parsing with object instances and occlusion ordering
Tighe, J., Niethammer, M., Lazebnik, S.: · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., Bengio, Y.: · 2014
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Recurrent models of visual attention
Mnih, V., Heess, N., Graves, A., et al.: · 2014
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Instance segmentation of indoor scenes using a coverage loss
Silberman, N., Sontag, D., Fergus, R.: · 2014
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Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 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
Draw: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Wierstra, D.: · 2015
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Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: · 2015
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Deep visual-semantic alignments for generating image descriptions
Karpathy, A., Fei-Fei, L.: · 2015
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Show and tell: A neural image caption generator
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Convolutional lstm networks for subcellular localization of proteins
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End-to-end people detection in crowded scenes
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Image-based plant phenotyping with incremental learning and active contours
Minervini, M., Abdelsamea, M.M., Tsaftaris, S.A.: · 2014
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3-d histogram-based segmentation and leaf detection for rosette plants
Pape, J.M., Klukas, C.: · 2014
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Multi-leaf tracking from fluorescence plant videos
Yin, X., Liu, X., Chen, J., Kramer, D.M.: · 2014
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Multi-instance object segmentation with occlusion handling
Chen, Y.T., Liu, X., Yang, M.H.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Semantic image segmentation with deep convolutional nets and fully connected crfs
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Recurrent convolutional neural networks for object-class segmentation of rgb-d video
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Multiple object recognition with visual attention
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Finely-grained annotated datasets for image-based plant phenotyping
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Leaf segmentation in plant phenotyping: A collation study
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Show, attend and tell: Neural image caption generation with visual attention
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