Detection of out-of-distribution (OoD) samples in the context of image classification has recently become an area of interest and active study, along with the topic of uncertainty estimation, to which it is closely related. In this paper we explore the task of OoD segmentation, which has been studied less than its classification counterpart and presents additional challenges. Segmentation is a dense prediction task for which the model's outcome for each pixel depends on its surroundings. The receptive field and the reliance on context play a role for distinguishing different classes and, correspondingly, for spotting OoD entities. We introduce MOoSe, an efficient strategy to leverage the various levels of context represented within semantic segmentation models and show that even a simple aggregation of multi-scale representations has consistently positive effects on OoD detection and uncertainty estimation.
翻译:最近,在图像分类中检测分配外(OoD)样本的工作与不确定性估计专题密切相关,成为令人感兴趣和积极研究的领域,与此密切相关。本文探讨OoD分解的任务,研究的OoD分解少于分类对应方,并提出了更多的挑战。分解是一项密集的预测任务,模型对每个像素的结果取决于其周围环境。接受字段和对背景的依赖在区分不同类别和相应地发现OoD实体方面发挥了作用。我们引入了MOoSe,这是一个有效的战略,以利用语义分解模型中代表的各种环境级别,并表明即使是简单的多尺度表示组合也会对OOD的探测和不确定性估计产生持续的积极影响。