This paper presents an approach to detect out-of-context (OOC) objects in an image. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the scene context and detect the OOC object with a bounding box. In this work, we consider commonly explored contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations. In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects.

本文提出了一种基于图的上下文推理网络（GCRN）检测图像中的不符上下文（OOC）物体的方法, GCRN可以明确捕捉到上下文线索，以改进检测位于上下文中的物体并识别违反上下文关系的物体。作者们创建了一个大规模数据集来评估他们的方法，并在最近的 OCD 基准测试上进行了评估。研究结果表明，GCRN 在检测 OOC 物体和正确检测位于上下文中的物体方面优于竞争基线。

利用上下文线索检测上下文外对象