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Connectivity-constrained Interactive Panoptic Segmentation

arXiv 2212.06756 ↗First posted 13 Dec 2022cs.CVcited by 0

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Abstract (arXiv)

We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce connectivity of each region, with a notable class-aware Integer Linear Programming (ILP) formulation that ensures global optimum. Both algorithms can take RGB, or utilize the feature maps from any DCNN, whether trained on the target dataset or not, as input. We then propose an interactive, scribble-based annotation framework.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 54 references that are in the map. First posted on arXiv 13 Dec 2022.

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30 of 54 references.

References · 30

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18 more
  1. 2017Dilated Residual Networkscited by 293
  2. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
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  12. 2016Superpixels: An Evaluation of the State-of-the-Artcited by 52
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  16. 2016The Minimum Cost Connected Subgraph Problem in Medical Image Analysiscited by 2
  17. 2017An ILP Solver for Multi-label MRFs with Connectivity Constraintscited by 2
  18. 2018Discrete Potts Model for Generating Superpixels on Noisy Imagescited by 1

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