Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Extreme Cut: From Extreme Points to Object Segmentation (2017, neutral): “Closer to our approach, [3] employs point-level supervision in the form of a single click to train a CNN for semantic segmentation and [25] use central points of an imaginary bounding box to weakly supervise object detection.”
Pointly-Supervised Instance Segmentation (2021, background): “Point-based supervision has been studied in a variety of tasks, including action localization [37, 38], object detection [42, 46], object counting [27], and semantic segmentation [4, 44].”
Large-Scale Interactive Object Segmentation With Human Annotators (2019, methodology): “A flurry of techniques have been explored to annotate the location of objects in images: bounding box annotations [8], clicks on the object extremes [26, 22], clicks on the object centre [3, 27], bounding box followed by edits to a generated segment [4, 30], scribbles [3, 18], hierarchical super-pix”
Object Detection With Deep Learning: A Review (2018, background): “center-click annotations [223]) are also helpful for achieving high-quality detectors with modest annotation efforts, especially aided by the mobile platform.”
Efficient Object Annotation via Speaking and Pointing (2019, methodology): “It further provides information that can be used as input to weakly-supervised methods (Bearman et al. 2016; Papadopoulos et al. 2017b).”