SuperAnnotate Webinar

Automated CV Pipelines

Box to Polygon (Epoch 5)

June 2, 10 am PT / 7 pm CET

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Webinar hosts

oscar ornberg

Oscar Örnberg

Solutions Engineering Team Lead at SuperAnnotate

David Arakelyan

ML Project Coordinator
vahan petrosyan

Vahan Petrosyan

Co-founder & CTO at SuperAnnotate

Webinar guests

Arsine Mangasaryan

Machine Learning Product Manager at SuperAnnotate

Luis Riera

AI Solutions Engineer at SuperAnnotate

Erik Harutyunyan

Machine Learning Engineer at SuperAnnotate

Vahagn Tumanyan

Machine Learning Engineer at SuperAnnotate

Sally-Ann Delucia

AI Solutions Engineer at SuperAnnotate

Armen Gyurjinyan

Machine Learning Engineer at SuperAnnotate

Instance segmentation is a major research topic in computer vision, therefore, having high-quality, large datasets is of paramount importance.

The most popular way of annotating instances is to use polygons for each instance.

Polygons can consist of a number of vertices ranging from just three to more than a hundred. They can have open spaces (holes) in them. Generally. their annotation is one of the most time-consuming aspects of the entire process. In fact, compared with another popular annotation task, such as object recognition with bounding boxes, the difference in the time spent annotating one instance is quite significant. It can (without any exaggerations) take 20 times more to annotate the given instance with a polygon compared to annotating it with a bounding box.

With this being said, a lot of research went into the automatic instance segmentation given the limited information about the object, e.g. its bounding box. It would be very time-efficient to simply annotate the bounding box and have a Neural Network (or another method) finish the annotation of the polygon and have the annotator fix the mistakes.

In Epoch 5, we are going to discuss such methods as well as how they can be implemented using the SuperAnnotate platform.

  • Named Entity Recognition/NLP (bounding box annotation of named entities)
  • Optical Character Recognition/OCR (character annotation of the words from the annotated bounding box)

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