论文标题
使用元学习的几个场景自适应人群计数
Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning
论文作者
论文摘要
我们考虑了几个场景自适应人群计数的问题。给定目标摄像头现场，我们的目标是将模型调整到该特定场景中，只有几个标记的该场景的图像。解决此问题的解决方案在许多现实世界中都有潜在的应用程序，我们理想地希望部署专门适用于目标摄像机的人群计数模型。我们通过在几乎没有制度的背景下从最近引入的学习对学习范式中汲取灵感来实现这一挑战。在培训中，我们的方法以促进对目标场景的快速适应的方式学习模型参数。在测试时，给定一个带有少数标记数据的目标场景，我们的方法迅速适应了该场景，并对学习的参数进行了一些梯度更新。我们广泛的实验结果表明，所提出的方法在几个场景中的自适应人群计数中优于其他替代方案。代码可在https://github.com/maheshkkumar/fscc上找到。
We consider the problem of few-shot scene adaptive crowd counting. Given a target camera scene, our goal is to adapt a model to this specific scene with only a few labeled images of that scene. The solution to this problem has potential applications in numerous real-world scenarios, where we ideally like to deploy a crowd counting model specially adapted to a target camera. We accomplish this challenge by taking inspiration from the recently introduced learning-to-learn paradigm in the context of few-shot regime. In training, our method learns the model parameters in a way that facilitates the fast adaptation to the target scene. At test time, given a target scene with a small number of labeled data, our method quickly adapts to that scene with a few gradient updates to the learned parameters. Our extensive experimental results show that the proposed approach outperforms other alternatives in few-shot scene adaptive crowd counting. Code is available at https://github.com/maheshkkumar/fscc.
