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AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
We address the problem of image-based crowd counting. In particular, we ...
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Few-shot Scene-adaptive Anomaly Detection
We address the problem of anomaly detection in videos. The goal is to id...
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A-cCCNN: adaptive ccnn for density estimation and crowd counting
Crowd counting, for estimating the number of people in a crowd using vis...
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Crowd Counting by Adapting Convolutional Neural Networks with Side Information
Computer vision tasks often have side information available that is help...
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Adaptive Scenario Discovery for Crowd Counting
Crowd counting, i.e., estimation number of pedestrian in crowd images, i...
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Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)
The problem of automated crowd segmentation and counting has garnered si...
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Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
Unsupervised crowd counting is a challenging yet not largely explored ta...
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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.
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