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Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning

2020-03-13 18:52:40
Mahesh Kumar Krishna Reddy, Mohammad Hossain, Mrigank Rochan, Yang Wang

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2002.00264

PDF

https://arxiv.org/pdf/2002.00264.pdf


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