Current two-stage object detectors, which mainly consist of a region proposal stage and a proposal recognition stage, may produce unreliable results for objects appearing with little information such as small and occluded objects. This is caused by poor region proposals and inaccurate proposal recognition. To address this problem, we propose a context augmentation algorithm that fully utilizes contextual information to generate high-quality region proposals and detection results. First, Region proposals are produced by two steps: 1) generate a coarse set of region proposals, some of which are reliable and some of which are ambiguous, and 2) the ambiguous region proposals are re-estimated using appearance and geometry information with respect to the reliable region proposals from step 1). Second, similar types of pair-wise relations between region proposals are used to produce global feature information associated with the region proposals in order to enhance recognition results. In practice, our method effectively improves the quality of region proposals as well as recognition results. Empirical studies show that the proposed context augmentation yields substantial and consistent improvements over baseline Faster R-CNN. Moreover, there is around 1.3% mAP improvement over Mask R-CNN on COCO dataset.

当前的两阶段目标检测器，主要由区域提议阶段和提议识别阶段组成，对于信息很少的目标，例如小和被遮挡的目标，可能会产生不可靠的结果。这是由糟糕的区域提案和不准确的提案识别引起的。为了解决这个问题，我们提出了一种上下文增强算法，它充分利用上下文信息来生成高质量的区域提议和检测结果。首先，区域提议由两个步骤产生：1) 生成一组粗略的区域提议，其中一些是可靠的，其中一些是模糊的，2) 使用外观和几何信息重新估计模糊的区域提议到步骤 1) 中的可靠区域建议。第二，区域提议之间相似类型的成对关系用于产生与区域提议相关联的全局特征信息，以增强识别结果。在实践中，我们的方法有效地提高了区域提议的质量以及识别结果。实证研究表明，与基线 Faster R-CNN 相比，所提出的上下文增强产生了实质性且一致的改进。此外，在 COCO 数据集上，mAP 比 Mask R-CNN 提高了约 1.3%。实证研究表明，与基线 Faster R-CNN 相比，所提出的上下文增强产生了实质性且一致的改进。此外，在 COCO 数据集上，mAP 比 Mask R-CNN 提高了约 1.3%。实证研究表明，与基线 Faster R-CNN 相比，所提出的上下文增强产生了实质性且一致的改进。此外，在 COCO 数据集上，mAP 比 Mask R-CNN 提高了约 1.3%。