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.
翻译:物体永久性是物体不会在物理世界中突然消失的概念。 人类在年轻时理解这个概念,并且知道另一个人仍然在那里, 尽管它只是暂时隐蔽的。 神经网络目前常常要面对这一挑战。 因此, 我们把明确的物体永久性引入两个阶段的探测方法, 从粒子过滤器中汲取灵感。 在核心, 我们的探测器用对先前框架的预测作为当前框架的附加建议。 实验证实了反馈循环改善探测性能, 由最高达10.3 mAP的微小计算管理器进行。 我们的方法适合扩大两阶段的探测器, 用于稳定可靠的探测, 即使是在严重封闭状态下。 此外, 应用我们的方法而无需再培训, 现有模型的能力可以保证在现实世界的任务中广泛应用。