分布实例分割：使用Latent-MaskRCNN建模不确定性与高置信度预测
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN
AI总结：
针对现有实例分割方法在模糊场景下难捕捉不确定性易引发严重错误的问题，提出带隐编码的分布实例分割模型Latent-MaskRCNN及置信度掩码方法，在新数据集和真实服装抓取机器人上验证了其减少错误、保持高性能的效果。
摘要：
目标识别与实例分割是所有机器人或自主系统的基础能力。现有最先进方法往往无法在具有挑战性或模糊的场景中捕捉有意义的不确定性，因此可能在高性能应用中引发严重错误。本文中，我们探索了一类使用隐编码的分布实例分割模型，这类模型能够对物体掩码的合理假设的不确定性进行建模。针对机器人抓取应用，我们提出了一种置信度掩码方法，以实现工业用例所需的高精度。我们表明，我们的方法能够显著减少机器人系统中的严重错误，包括在我们新发布的机器人应用模糊场景数据集上的表现。在真实世界的服装抓取机器人上，我们的方法在保持高性能的同时，显著减少了双重抓取错误。
英文摘要：
Object recognition and instance segmentation are fundamental skills in any robotic or autonomous system. Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications. In this paper, we explore a class of distributional instance segmentation models using latent codes that can model uncertainty over plausible hypotheses of object masks. For robotic picking applications, we propose a confidence mask method to achieve the high precision necessary in industrial use cases. We show that our method can significantly reduce critical errors in robotic systems, including our newly released dataset of ambiguous scenes in a robotic application. On a real-world apparel-picking robot, our method significantly reduces double pick errors while maintaining high performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
