In weakly supervised object detection, most existing approaches are proposed for images. Without box-level annotations, these methods cannot accurately locate objects. Considering an object may show different motion from its surrounding objects or background, we leverage motion information to improve the detection accuracy. However, the motion pattern of an object is complex. Different parts of an object may have different motion patterns, which poses challenges in exploring motion information for object localization. Directly using motion information may degrade the localization performance. To overcome these issues, we propose a Motion Context Network (MC-Net) in this letter. Our method generates motion context features by exploiting neighborhood motion correlation information on moving regions. These motion context features are then incorporated with image information to improve the detection accuracy. Furthermore, we propose a temporal aggregation module, which aggregates features across frames to enhance the feature representation at the current frame. Experiments are carried out on ImageNet VID, which shows that our MC-Net significantly improves the performance of the image based baseline method (37.4% mAP v.s. 29.8% mAP).

中文翻译：

---

用于视频中弱监督目标检测的运动上下文网络

在弱监督目标检测中，大多数现有方法都是针对图像提出的。如果没有框级注释，这些方法无法准确定位对象。考虑到一个物体可能会表现出与其周围物体或背景不同的运动，我们利用运动信息来提高检测精度。然而，物体的运动模式是复杂的。对象的不同部分可能具有不同的运动模式，这对探索用于对象定位的运动信息提出了挑战。直接使用运动信息可能会降低定位性能。为了克服这些问题，我们在这封信中提出了一个运动上下文网络（MC-Net）。我们的方法通过利用运动区域的邻域运动相关信息来生成运动上下文特征。然后将这些运动上下文特征与图像信息结合起来以提高检测精度。此外，我们提出了一个时间聚合模块，该模块跨帧聚合特征以增强当前帧的特征表示。在 ImageNet VID 上进行了实验，这表明我们的 MC-Net 显着提高了基于图像的基线方法的性能（37.4% mAP vs 29.8% mAP）。