Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression (1902.09630v2)

Abstract: Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

• The paper proposes Generalized Intersection over Union (GIoU) to address shortcomings of the standard IoU in evaluating bounding box overlap.
• It introduces a differentiable loss function that optimizes bounding box predictions and enhances localization accuracy.
• Experimental results validate that GIoU outperforms traditional IoU metrics, providing consistent performance gains in detection tasks.

Overview of the CVPR Proceedings Author Guidelines

In the paper titled "LaTeX Author Guidelines for CVPR Proceedings," the authors provide comprehensive instructions for preparing manuscripts aimed at the IEEE Computer Society's Computer Vision and Pattern Recognition (CVPR) conference. The guidelines are structured to ensure consistency, readability, and adherence to the conference's stringent submission and review processes.

Formatting and Length Specifications

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Adherence to these guidelines has practical and theoretical implications for the CVPR community. Structurally consistent manuscripts enhance readability and facilitate a more efficient review process. This is particularly beneficial for a conference like CVPR, which receives a high volume of submissions.

On a theoretical level, these guidelines support the formal communication of advancements in computer vision, ensuring that complex ideas are presented in a clear and standardized format. This fosters better comprehension and cross-pollination of ideas among researchers.

Future Developments

Looking ahead, the implications of such standardized guidelines might extend to other conferences and journals, encouraging a more uniform approach to research dissemination across the field. As AI research evolves, the necessity for clear, consistent presentation of data and methodology will likely become even more pronounced, possibly prompting further refinements to these guidelines.

In sum, the "LaTeX Author Guidelines for CVPR Proceedings" paper serves as an important resource for authors, ensuring submissions meet the high standards of clarity, consistency, and academic rigor required by the CVPR community.