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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Cluster2Former: Semisupervised Clustering Transformers for Video Instance Segmentation.

Áron Fóthi1, Adrián Szlatincsán1, Ellák Somfai1,2

  • 1Department of Artificial Intelligence, ELTE Eötvös Loránd University, 1053 Budapest, Hungary.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Cluster2Former, a semisupervised learning method for video instance segmentation. It uses minimal scribble annotations to achieve competitive results, reducing data labeling costs.

Keywords:
instance segmentationsemisupervised learningtransformersvideo processing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video instance segmentation is crucial for understanding dynamic scenes.
  • Traditional methods require extensive pixel-level annotations, which are costly and time-consuming.
  • Semisupervised learning offers a potential solution to reduce annotation burden.

Purpose of the Study:

  • To develop a novel semisupervised approach for video instance segmentation.
  • To reduce the reliance on fully annotated datasets by utilizing lightweight annotations.
  • To improve the cost-effectiveness and efficiency of video instance segmentation.

Main Methods:

  • Proposed the Cluster2Former model, augmenting existing architectures like Mask2Former.
  • Employed scribble-based annotations for training.
  • Introduced a similarity-based constraint loss to effectively handle partial annotations.

Main Results:

  • Achieved competitive performance on standard video instance segmentation benchmarks.
  • Demonstrated effectiveness with as little as 0.5% of annotated pixels.
  • Showcased the model's ability to handle limited annotation resources.

Conclusions:

  • Cluster2Former provides a viable and efficient solution for video instance segmentation.
  • The approach significantly lowers annotation costs and computational requirements.
  • It is particularly beneficial for applications with scarce labeled data.