Bi-level Alignment for Cross-Domain Crowd Counting
- URL: http://arxiv.org/abs/2205.05844v1
- Date: Thu, 12 May 2022 02:23:25 GMT
- Title: Bi-level Alignment for Cross-Domain Crowd Counting
- Authors: Shenjian Gong, Shanshan Zhang, Jian Yang, Dengxin Dai and Bernt
Schiele
- Abstract summary: Current methods rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation.
We develop a new adversarial learning based method, which is simple and efficient to apply.
We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin.
- Score: 113.78303285148041
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, crowd density estimation has received increasing attention. The
main challenge for this task is to achieve high-quality manual annotations on a
large amount of training data. To avoid reliance on such annotations, previous
works apply unsupervised domain adaptation (UDA) techniques by transferring
knowledge learned from easily accessible synthetic data to real-world datasets.
However, current state-of-the-art methods either rely on external data for
training an auxiliary task or apply an expensive coarse-to-fine estimation. In
this work, we aim to develop a new adversarial learning based method, which is
simple and efficient to apply. To reduce the domain gap between the synthetic
and real data, we design a bi-level alignment framework (BLA) consisting of (1)
task-driven data alignment and (2) fine-grained feature alignment. In contrast
to previous domain augmentation methods, we introduce AutoML to search for an
optimal transform on source, which well serves for the downstream task. On the
other hand, we do fine-grained alignment for foreground and background
separately to alleviate the alignment difficulty. We evaluate our approach on
five real-world crowd counting benchmarks, where we outperform existing
approaches by a large margin. Also, our approach is simple, easy to implement
and efficient to apply. The code is publicly available at
https://github.com/Yankeegsj/BLA.
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