Simultaneous Edge Alignment and Learning (1808.01992v3)

Abstract: Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications. Recent advances in representation learning have led to considerable improvements in this area. Many state of the art edge detection models are learned with fully convolutional networks (FCNs). However, FCN-based edge learning tends to be vulnerable to misaligned labels due to the delicate structure of edges. While such problem was considered in evaluation benchmarks, similar issue has not been explicitly addressed in general edge learning. In this paper, we show that label misalignment can cause considerably degraded edge learning quality, and address this issue by proposing a simultaneous edge alignment and learning framework. To this end, we formulate a probabilistic model where edge alignment is treated as latent variable optimization, and is learned end-to-end during network training. Experiments show several applications of this work, including improved edge detection with state of the art performance, and automatic refinement of noisy annotations.

• The paper introduces SEAL, a probabilistic model that jointly optimizes latent edge alignment and learning to overcome label misalignment in edge detection tasks.
• It reformulates edge detection as a bipartite graph min-cost assignment problem, achieving sharper edges and improved metrics on datasets like SBD and Cityscapes.
• The approach challenges conventional reweighted loss functions, paving the way for further research in structured noisy label learning for high-precision computer vision.

Simultaneous Edge Alignment and Learning: A Comprehensive Overview

This paper addresses a persistent issue in edge detection tasks within computer vision—the challenge posed by label misalignment. The proposed Simultaneous Edge Alignment and Learning (SEAL) framework integrates edge alignment as a latent variable optimization problem within the fully convolutional networks (FCNs) framework. This integration ensures that edge alignment and learning occur simultaneously during network training, offering a potential solution to the misalignment problem that degrades learning quality in edge detection applications.

Key Contributions

The paper's central contribution is the development of SEAL, a probabilistic model that allows edge alignment and learning to occur simultaneously. SEAL formulates edge detection as a problem in which edge alignment is treated as a latent variable that must be optimized. The proposed framework converts the optimization problem into a bipartite graph min-cost assignment issue, which can be solved using standard algorithms.

Numerical results highlight SEAL's effectiveness. The framework achieved improved edge detection on datasets like the Semantic Boundary Dataset (SBD) and Cityscapes, yielding high-quality, sharp edges and outperforming state-of-the-art models like CASENet. This improvement is quantified using metrics such as maximum F-Measure (MF) and average precision (AP), demonstrating SEAL's ability to produce superior results, particularly when dealing with imprecise and noisy edge labels.

Practical and Theoretical Implications

The practical implications of this work are significant for applications that rely on precise edge detection, such as semantic segmentation, object detection, and 3D vision. By improving edge representation quality, SEAL can enhance the performance of these higher-level vision tasks, which depend on accurate feature extraction.

Theoretically, the paper challenges the conventional wisdom that reweighted loss functions are essential for handling imbalanced datasets in edge learning. SEAL's findings suggest that the incorporation of edge alignment reduces sample confusion, allowing the use of regular sigmoid cross-entropy loss to produce state-of-the-art results. This insight could lead to a reevaluation of loss function design in similar problems.

The integration of misalignment handling directly into the learning process introduces a paradigm shift in how edge detectors are trained and evaluated. By considering structural patterns during optimization, SEAL indicates potential for further research in structured noisy label learning, particularly in domains where precise labeling is difficult due to annotation costs or human error.

Future Developments in AI

The framework proposed in this paper may inspire developments in AI, particularly in areas requiring high precision and adaptability to noisy data. Future research can focus on extending SEAL's approach to other types of labeling tasks in vision and beyond, aiming to harness latent variable optimization for improved accuracy without increasing data annotation demands.

Exploration into optimizing the computational cost of SEAL, especially regarding the number of Assign steps and their impact on training efficiency, could advance deployment in real-time applications. Furthermore, adapting this model for unsupervised or semi-supervised contexts might broaden its applicability, supporting edge detection in novel environments where labels are sparse or unavailable.

In summary, the SEAL framework provides a robust methodology for addressing misaligned labels in edge detection, promising enhancements in both performance and the theoretical understanding of edge learning and label alignment within deep learning frameworks.