Semi-Supervised Crowd Counting via Multi-Task Pseudo-Label Self-Correction Strategy

Currently, existing semi-supervised crowd counting methods usually learn unlabeled images through pseudo-labels and spatial consistency regularization paradigm. However, due to extremely limited labeled data and noise in density map pseudo-labels, the counting performance of the model is greatly limited. Although multi-task learning can help the model improve its feature representation ability, it seriously ignores the importance of multi-task collaboration. Therefore, to overcome the above problems, we propose a multi-task pseudo-label self-correction (MTPS) framework for crowd counting, which combines different tasks to enhance the correlation between tasks and reduce training bias. For labeled data, multi-task collaboration enables the model to fully explore the potential information in limited samples; for unlabeled data, multi-task collaboration can obtain more accurate pseudo-labels than density estimation task. In addition, to effectively suppress the problem of inaccurate pseudo-labels caused by noise, we propose a pseudo-label self-correction strategy based on multi-task collaboration. This strategy starts from the perspective of task to task to gradually reduce the interference of background noise and obtain higher quality pseudo-labels. A large number of experiments on three public datasets show that the proposed MTPS achieves superior counting performance.