Leaf segmentation in plant phenotyping: a collation study

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Recurrent Instance Segmentation (2015, methodology): “At inference time we assign a pixel to an instance if the predicted value is higher than 0.5.”
Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “…contests (see Section 4) using a subset of the annotated data presented here showed that the number of available images and criteria used allowed computer vision scientists to work successfully on these problems (see e.g. (Scharr et al., 2015; Pape and Klukas, 2014, 2015; Giu ﬀ rida et al., 2015)).”
Semantic Instance Segmentation with a Discriminative Loss Function (2017, methodology): “[32] gives an overview of results on this dataset.”
End-to-End Instance Segmentation and Counting with Recurrent Attention (2016, neutral): “We compare our performance to [18], and other top approaches that were published with the CVPPP conference; see the collation study [19] for details of these other approaches.”
End-to-End Instance Segmentation with Recurrent Attention (2016, result): “We compare our performance to [35], and other top approaches that were published with the CVPPP conference; see the collation study [36] for details of these other approaches.”