Cell-Wise Self-Optimization: Making Pretrained Model Better in Remote Sensing Counting

Recently, remote sensing counting has drawn a lot of attention due to its wide application requirements. However, most existing approaches tend to focus on optimizing the network backend or designing new loss functions, and overlook a foundational component, i.e., the feature extractor, which is usually based on a pre-trained model. This oversight limits the potential for further performance improvement. In this paper, we propose a cell-wise self-optimization method to enhance the feature extractor. By leveraging the powerful representation capabilities of a pre-trained model, our method further refines them for counting tasks, notably improving network performance on limited remote sensing data. Specifically, we design a lightweight cell-wise architecture optimization based on a network architecture search algorithm. It sequentially builds lightweight cells in parallel with the blocks in a pre-trained model, leveraging a newly proposed random path selection strategy for training the optimization framework. Additionally, we propose extracting high-frequency information from the blocks in a pre-trained model as self-guidance optimization to facilitate the learning of the searched cells. Experimental results on several representative datasets demonstrate that our proposed method significantly improves counting performance while substantially reducing parameters. For instance, compared with our baseline, it improves performance by 22.2% in MAE and 17.0% in MSE on the Building dataset, while reducing parameters by 61.2%.