Shot Object Counting

Shot object counting aims to accurately estimate the number of objects in an image, given a limited number of example images (few-shot) or even none (zero-shot). Current research heavily focuses on improving object localization and count estimation accuracy through novel architectures like transformers and those incorporating both detection and segmentation, often employing techniques such as mutually-aware feature learning and iterative prototype adaptation to enhance feature matching and robustness. These advancements are significant for applications requiring efficient object counting in scenarios with limited labeled data, such as environmental monitoring or automated inventory management.