mlsanityData to 14 Oct 2026

AdaContour: Adaptive Contour Descriptor with Hierarchical Representation

arXiv 2404.08292 ↗First posted 12 Apr 2024cs.CV cs.GRcited by 0

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Abstract (arXiv)

Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single global inner center and a set of radii corresponding to a polar coordinate parameterization. In this paper, we propose AdaContour, an adaptive contour descriptor that uses multiple local representations to desirably characterize complex shapes. After hierarchically encoding object shapes in a training set and constructing a contour matrix of all subdivided regions, we compute a robust low-rank robust subspace and approximate each local contour by linearly combining the shared basis vectors to represent an object. Experiments show that AdaContour is able to represent shapes more accurately and robustly than other descriptors while retaining effectiveness. We validate AdaContour by integrating it into off-the-shelf detectors to enable instance segmentation which demonstrates faithful performance. The code is available at https://github.com/tding1/AdaContour.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 73 references that are in the map. First posted on arXiv 12 Apr 2024.

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27 of 73 references.

References · 27

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15 more
  1. 2019Fast Interactive Object Annotation with Curve-GCNcited by 95
  2. 2019PolyTransform: Deep Polygon Transformer for Instance Segmentationcited by 92
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  14. 2023CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Renderingcited by 10
  15. 2023InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modulescited by 7

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