通过多尺度置信度检测未知物体，提升分割模型在开放世界中的可靠性。
Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation
- 利用前景-背景模型的像素置信度，构建多尺度检测机制。
- 在SegmentMeIfYouCan基准上优于现有基线方法，显著提升未知类别分割性能。
- 适合自动驾驶等安全关键场景，用于识别训练数据外的未知物体。
深度神经网络在语义分割等计算机视觉任务中表现卓越，已达到当前最优水平。然而，这些模型通常在预定义的封闭类别集合上训练，导致在开放世界中面对未知物体时出现严重预测失败，限制了其在自动驾驶等安全关键应用中的使用。因此，检测并分割超出预定义语义空间的物体（即分布外，OOD）至关重要。本文提出一种多尺度的OOD分割方法，利用前景-背景分割模型的置信度信息。尽管语义分割模型受限于特定类别，但前景-背景方法无此限制，更适合处理未知物体。我们分析每个像素的置信度得分——接近1表示属于前景物体。通过聚合不同尺寸块的置信度值，可在单张图像中识别出各种尺寸的目标。实验表明，该方法在SegmentMeIfYouCan基准上优于可比基线，显著提升了对分布外物体的分割性能。
原文摘要 · Abstract (English)
Deep neural networks have shown outstanding performance in computer vision tasks such as semantic segmentation and have defined the state-of-the-art. However, these segmentation models are trained on a closed and predefined set of semantic classes, which leads to significant prediction failures in open-world scenarios on unknown objects. As this behavior prevents the application in safety-critical applications such as automated driving, the detection and segmentation of these objects from outside their predefined semantic space (out-of-distribution (OOD) objects) is of the utmost importance. In this work, we present a multi-scale OOD segmentation method that exploits the confidence information of a foreground-background segmentation model. While semantic segmentation models are trained on specific classes, this restriction does not apply to foreground-background methods making them suitable for OOD segmentation. We consider the per pixel confidence score of the model prediction which is close to 1 for a pixel in a foreground object. By aggregating these confidence values for different sized patches, objects of various sizes can be identified in a single image. Our experiments show improved performance of our method in OOD segmentation compared to comparable baselines in the SegmentMeIfYouCan benchmark.
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