Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
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Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to reduce the domain shift between the source and target domain. Recently, typical methods attempt to extract domain-invariant features via image translation and adversarial learning. When it comes to specific tasks, we find that the final manifestation of the task gap is in the parameters of the model, and the domain shift can be represented apparently by the differences in model weights. To describe the domain gap directly at the parameter-level, we propose a Neuron Linear Transformation (NLT) method, where NLT is exploited to learn the shift at neuron-level and then transfer the source model to the target model. Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn a group of parameters, which updates the target neuron via a linear transformation. Extensive experiments and analysis on six real-world datasets validate that NLT achieves top performance compared with other domain adaptation methods. An ablation study also shows that the NLT is robust and more effective compare with supervised and fine-tune training. Furthermore, we will release the code after the paper is accepted.
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