Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning

• URL: http://arxiv.org/abs/2002.00264v3
• Date: Fri, 19 Jun 2020 05:54:24 GMT
• Title: Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning
• Authors: Mahesh Kumar Krishna Reddy, Mohammad Hossain, Mrigank Rochan and Yang Wang
• Abstract summary: 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. We accomplish this challenge by taking inspiration from the recently introduced learning-to-learn paradigm in the context of few-shot regime.
• Score: 13.149654626505741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Code is available at https://github.com/maheshkkumar/fscc.

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