CSTrans: Correlation-guided Self-Activation Transformer for Counting Everything

Abstract

Counting everything, also named few-shot counting, requires a model to be able to count objects with any novel (unseen) category giving few exemplar boxes. However, the existing few-shot counting methods are sub-optimal due to weak feature representation, such as the correlation between the exemplar patch and query feature, and contextual dependencies in density map prediction. In this paper, we propose a very simple but effective method, CSTrans, consisting of a Correlation-guided Self-Activation (CSA) module and a Local Dependency Transformer (LDT) module, to mitigate the above two issues, respectively. The CSA utilizes the correlation map to activate the semantic features and suppress the noisy influence of the query features, aiming at mining the potential relation while enriching correlation representation. Furthermore, the LDT incorporates a Transformer to explore local contextual dependencies and predict the density map. Our method achieves competitive performance on FSC-147 and CARPK datasets. We hope its simple implementation and superior performance can serve as a new and strong baseline for few-shot counting tasks and attract more interest in designing simple but effective models in future studies. Our code for CSTrans is available at https://github.com/gaobb/CSTrans.