Class Agnostic Counting

Class-agnostic counting (CAC) aims to automatically estimate the number of objects of any category in an image, even those unseen during training, a significant advance over traditional category-specific counting. Current research focuses on improving accuracy and robustness, particularly in multi-class scenarios, using techniques like density map estimation, attention mechanisms within transformer networks, and novel loss functions designed to address biases in existing datasets. These advancements hold promise for applications requiring efficient and generalizable object counting across diverse visual scenes, such as crowd analysis, wildlife monitoring, and automated inventory management.