A Straightforward Approach to Training-free Class-Agnostic Counting

The author presents a training-free solution for Class-Agnostic Counting, bridging the performance gap with trained models by utilizing superpixels, semantic-rich encoders, multi-scale mechanisms, and transductive prototype updating. This approach achieves performance on par with training-based methods.