论文标题
物体检测中的对象永久性在推理时间利用时间较先验
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time
论文作者
论文摘要
对象永久性是物体不会突然消失在物理世界中的概念。人类在年轻时了解这个概念，并且知道另一个人仍在那里，即使它暂时被遮住了。目前，神经网络经常在这一挑战中挣扎。因此，我们将显式对象持久性引入两个阶段检测方法，从粒子过滤器中汲取灵感。核心，我们的检测器将对先前框架的预测用作推理时间当前框架的其他建议。实验证实了反馈循环，可通过几乎没有计算开销的最多10.3地图提高检测性能。 我们的方法适合于延长两阶段探测器，即使在重闭塞下，也可以稳定且可靠的检测。此外，在不重新训练现有模型的情况下应用我们的方法的能力承诺在现实世界任务中进行广泛的应用。
Object permanence is the concept that objects do not suddenly disappear in the physical world. Humans understand this concept at young ages and know that another person is still there, even though it is temporarily occluded. Neural networks currently often struggle with this challenge. Thus, we introduce explicit object permanence into two stage detection approaches drawing inspiration from particle filters. At the core, our detector uses the predictions of previous frames as additional proposals for the current one at inference time. Experiments confirm the feedback loop improving detection performance by a up to 10.3 mAP with little computational overhead. Our approach is suited to extend two-stage detectors for stabilized and reliable detections even under heavy occlusion. Additionally, the ability to apply our method without retraining an existing model promises wide application in real-world tasks.
