Dynamic example network for class-agnostic object counting

This work addresses the class-agnostic counting and localization task, a critical challenge in computer vision where the goal is to count and locate objects of any category in an image using a few annotated examples. The primary challenge arises from the limited information on appearance due to the lack of diverse examples, which hampers the model's ability to generalize to varied object appearances. To tackle this issue, we propose a dynamic example network (DEN), consisting of a Location and Example Decoder module (LEDM) designed to incrementally expand the set of examples and refine predictions through multiple iterations. Additionally, our negative example mining strategy identifies informative negative examples across the entire dataset, further improving the model's discriminative capacity. Extensive experiments on five datasets—FSC-147, FSCD-LVIS, CARPARK, UAVCC, and Visdrone—demonstrate the effectiveness of our approach, showing marked improvements over several state-of-the-art methods. The source code and trained models will be publicly accessible to facilitate further research and application in the field.