Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning
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.
Code (1)
Tasks
Crowd CountingMeta-LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM
The existing crowd counting models require extensive training data, which is time-consuming to annotate. To tackle this issue, we propose a simple yet effective crowd counting method by utilizing the Segment-Everything-E…
Crowd CountingAdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
We address the problem of image-based crowd counting. In particular, we propose a new problem called unlabeled scene-adaptive crowd counting. Given a new target scene, we would like to have a crowd counting model specifi…
Crowd CountingOne-Shot Crowd Counting With Density Guidance For Scene Adaptation
Crowd scenes captured by cameras at different locations vary greatly, and existing crowd models have limited generalization for unseen surveillance scenes. To improve the generalization of the model, we regard different …
Few-Shot LearningCrowd CountingScene-Adaptive Attention Network for Crowd Counting
In recent years, significant progress has been made on the research of crowd counting. However, as the challenging scale variations and complex scenes existed in crowds, neither traditional convolution networks nor recen…
Crowd CountingA-CCNN: adaptive ccnn for density estimation and crowd counting
Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems…
Crowd CountingDensity EstimationObject Counting