RankED: Addressing Imbalance and Uncertainty in Edge Detection
Using Ranking-based Losses

Bedrettin Cetinkaya, Sinan Kalkan*, Emre Akbas*
Middle East Technical University
(*Equal contribution)

CVPR 2024

arXiv Code

Abstract

Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators. Existing solutions address P1 using class-balanced cross-entropy loss and dice loss and P2 by only predicting edges agreed upon by most annotators. In this paper, we propose RankED, a unified ranking-based approach that addresses both the imbalance problem (P1) and the uncertainty problem (P2). RankED tackles these two problems with two components: One component which ranks positive pixels over negative pixels, and the second which promotes high confidence edge pixels to have more label certainty. We show that RankED outperforms previous studies and sets a new state-of-the-art on NYUD-v2, BSDS500 and Multi-cue datasets.



Overview


(a) Current approaches threshold label certainties and
class-balanced cross-entropy loss for training edge detectors. 







(b) With RankED, we propose a unified approach which ranks
positives over negatives to handle the imbalance problem
and sorts positives with respect to their certainties.


Visual Results

RGB





GT





OURS

(AFTER NMS)




OURS
(AFTER NMS
+
Thinning)


Acknowledgements

We gratefully acknowledge the computational resources provided by METU-ROMER, Center for Robotics and Artificial Intelligence.

BibTeX


@inproceedings{cetinkaya2024ranked,
  title={RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses}, 
  author={Bedrettin Cetinkaya and Sinan Kalkan and Emre Akbas},
  year={2024},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  url={https://ranked-cvpr24.github.io/}
}