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

핵심 개념

RankED proposes a unified ranking-based approach to address the imbalance and uncertainty problems in edge detection, outperforming previous studies on various datasets.

초록

RankED introduces a novel ranking-based solution to tackle the challenges of imbalance and uncertainty in edge detection. By prioritizing positive pixels over negatives and sorting them based on certainty, RankED achieves state-of-the-art results on multiple datasets.
Existing solutions for edge detection face challenges due to class imbalance and label uncertainty. RankED addresses these issues with innovative ranking and sorting components, leading to superior performance compared to traditional methods.
The proposed method, RankED, combines ranking positive pixels over negatives with sorting based on certainty levels. This approach outperforms previous studies on NYUD-v2, BSDS500, and Multi-cue datasets.
RankED's unique approach of using ranking-based losses effectively tackles the imbalance between positive and negative classes as well as the uncertainty in edge annotations. The method sets a new state-of-the-art benchmark across multiple datasets.
By introducing a unified ranking-based solution, RankED successfully addresses the challenges of class imbalance and label uncertainty in edge detection tasks. The method outperforms existing approaches on popular datasets like NYUD-v2, BSDS500, and Multi-cue.
RankED presents a novel strategy for addressing class imbalance and label uncertainty in edge detection through innovative ranking and sorting components. The method surpasses previous studies by achieving new benchmarks on various datasets.

통계

In the BSDS dataset, only 7% of all pixels are marked as edge pixels.
RankED outperforms previous studies on NYUD-v2, BSDS500, and Multi-cue datasets.
RANKED consistently outperforms all SOTA models in average precision (AP).

인용구

"Existing solutions address P1 using class-balanced cross-entropy loss and dice loss."
"We propose RankED, a unified ranking-based approach that addresses both the imbalance problem (P1) and the uncertainty problem (P2)."
"Our main contributions are proposing RANKED for edge detection that simultaneously addresses imbalance and uncertainty issues."

핵심 통찰 요약

RankED

by Bedrettin Ce... 게시일 arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01795.pdf

더 깊은 질문

How can the concept of ranking-based losses be applied to other computer vision tasks beyond edge detection?

Ranking-based losses can be applied to various computer vision tasks beyond edge detection by adapting the core principles of prioritizing certain elements over others based on their importance or relevance. For instance:

Object Detection: In object detection, ranking-based losses can help prioritize the correct localization and classification of objects with higher confidence scores, leading to more accurate detections.
Semantic Segmentation: By ranking pixel-wise predictions based on their certainty or importance, semantic segmentation models can focus on accurately segmenting crucial regions in an image while de-emphasizing less significant areas.
Instance Segmentation: Similar to object detection, instance segmentation tasks can benefit from ranking instances based on their significance within a scene, improving the overall segmentation quality.

What potential limitations or drawbacks might arise from relying solely on ranking-based approaches for solving complex computer vision problems?

While ranking-based approaches offer several advantages, they also come with some limitations and drawbacks:

Sensitivity to Noisy Data: Ranking methods may amplify errors in noisy data since they heavily rely on relative comparisons between samples.
Difficulty in Handling Ambiguity: Complex scenes with ambiguous features may pose challenges for ranking algorithms as determining clear hierarchies among elements becomes challenging.
Increased Computational Complexity: Implementing sophisticated ranking mechanisms could increase computational overhead compared to simpler loss functions like cross-entropy.

How can incorporating human feedback or preferences into the training process impact the performance of models like RankED?

Incorporating human feedback or preferences into models like RankED can have several impacts on performance:

Improved Model Generalization: Human feedback helps provide valuable insights that guide model training towards learning relevant patterns and features present in real-world data.
Enhanced Robustness Against Annotator Disagreements: By considering multiple annotations and incorporating annotator agreement levels into training, models become more robust against label uncertainty and disagreement.
Bias Reduction Human feedback integration allows for bias correction during model training by aligning predictions with human perception standards, leading to fairer and more accurate results across diverse datasets.

By leveraging human input effectively during training, RankED and similar models can achieve better generalization capabilities, increased robustness against uncertainties, and reduced biases in predictions.