SAFe: 基于特征密度分段聚合的异常感知分割

中文摘要 AI 辅助

在真实世界部署中，视觉分割系统会遇到训练分布之外的物体，这阻碍了依赖感知阶段场景解析的可靠自主系统。许多近期方法通过使用自监督基础模型训练密度估计器来解决此问题，这些密度估计器在异常图像区域产生低似然度。尽管这些方法前景可观，但它们存在特征语义性差或缺乏空间一致性的问题，这两者都会破坏关键的下游决策。我们通过所提出的方法SAFe来解决这一问题，这是一种基于自监督表示上的类条件密度估计的生成方法。SAFe训练轻量级归一化流，在冻结的DINOv3特征上产生类条件归一化似然估计。我们将来自Transformer特征的密度估计与多尺度卷积特征上的密度分数相结合，以同时捕获全局语义和局部细节。我们引入了一种基于SAM3的方法无关后处理步骤，该步骤将逐位置的似然度连接成空间一致的片段，同时抑制误报，并支持无需重新训练的实例级异常检测。该后处理通过基于相似性的凝聚聚类方案进一步区分异常对象中的新类别。SAFe在PANIC、OoDIS、SMIYC ObstacleTrack基准上树立了新的最先进水平，并在ISSU基准上表现出强劲性能。

英文摘要

Visual segmentation systems encounter objects outside their training distribution during real-world deployment, hindering reliable autonomous systems that depend on scene parsing in the perception stage. Many recent methods address this by using self-supervised foundation models to train density estimators that yield low likelihood in anomalous image regions. Although promising, these methods suffer from poor feature semantics or they lack spatial consistency, both of which undermine critical downstream decisions. We address this problem with~\method, a generative method based on class-conditional density estimation over self-supervised representations. SAFe trains lightweight normalizing flows that produce class-conditional normalized likelihood estimates over frozen DINOv3 features. We combine density estimates from transformer features with density scores over multi-scale convolutional features to capture both global semantics and local detail. We introduce a method-agnostic post-processing step based on SAM3 that connects per-location likelihoods into spatially coherent segments while suppressing false positives, and enables instance-level anomaly detection without retraining. The post processing further distinguishes novel categories among anomalous objects by a similarity-based agglomerative clustering scheme. SAFe sets a new state of the art on the PANIC, OoDIS, SMIYC ObstacleTrack with strong performance on the ISSU benchmark.

发表机构

• University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）

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