Discrepancy-Controlled Region-Adaptive Learning: Handling Intra-Domain Bias for Crowd Counting

Crowd counting in congested scenarios remains challenging, when required to handle “intra-domain bias”—the significant variation in crowd density across regions within each image. In this study, we propose a novel Discrepancy-Controlled Region-Adaptive Learning (DC-RA) method which leverages a divide-and-conquer strategy, transforming the complex problem of image-level crowd counting into a series of more manageable regional tasks. Specifically, we propose a Discrepancy-Controlled Adaptive Partition (DCAP) module, to divide each image to regions that adapt to the varying density levels controlled by discrepancy of crowd density. To specify features for each region, the Region-wise Adaptive Learning (RAL) module is then introduced by incorporating the Mixture-of-Experts (MoE) framework, which involves using a routing module to select the most suitable expert for each region. This dynamic selection process ensures that each region benefits from tailored optimization based on its specific characteristics, leading to more precise density estimates. To ensure that each expert captures the distinct characteristics of various regions, we further incorporate a region-level counting loss for optimization. Experiments show that DC-RA reduces the Mean Absolute Errors (MAE) by 2.5 and 4.1 compared with the state-of-the-art method on JHU-CROWD++ and NWPU, respectively, significantly enhancing the model’s robustness and accuracy across varying crowd densities.