تقرير
Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning
| العنوان: | Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning |
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| المؤلفون: | Reddy, Mahesh Kumar Krishna, Hossain, Mohammad, Rochan, Mrigank, Wang, Yang |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted to WACV 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2002.00264 |
| رقم الأكسشن: | edsarx.2002.00264 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |