Learning to Detect Partially Overlapping Instances

By Carlos Arteta, Victor S. Lempitsky, J. Alison Noble, Andrew Zisserman. 2013

47 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Recurrent Instance Segmentation (2015, background): “Keywords: Instance segmentation, recurrent neural nets, deep learning”
Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “Due to size di ﬀ erences, shape and appearance similarities, and heavy occlusions, leaf detection is a complex task, and can beneﬁt from approaches in computer vision of detecting overlapping objects in medicine, transportation, and surveillance (Arteta et al., 2013; Wohlhart et al., 2013).”
Extremely Overlapping Vehicle Counting (2015, background): “We understand the extremely overlapping vehicle counting problem as a variant of the problem of crowd counting [7,8,9,13,14,15].”
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (2015, methodology): “Typical examples are the STAPLE algorithm [2], the log-odds maps [3] and their refinement [4].”
Learning to Count Leaves in Rosette Plants (2015, background): “The counting of overlapping objects is addressed in [3], even though varying object size remains an open issue.”
Annotated Image Datasets of Rosette Plants (2014, methodology): “We also do hope that this dataset and its future versions, would be used from the computer vision community to learn suitable image statistics [19], adapt and test counting algorithms with [20] and without temporal information [21, 22, 23], segmentation algorithms [24, 25, 26, 27, 28], multi-label s”
Interactive Object Counting (2014, methodology): “The objective (9) is optimized efficiently by using dynamic programming due to the tree structure of the regions as in [3].”
Where are the Blobs: Counting by Localization with Point Supervision (2018, methodology): “Similar to our method is the line of work proposed by Arteta et al. [28,29,30].”