Generalized-Scale Object Counting with Gradual Query Aggregation

The launch of GECO2 marks a significant advancement in the field of image processing, particularly in few-shot detection and object counting. Traditional methods often struggle with images containing a variety of object sizes and densely populated small objects, leading to inefficiencies. GECO2 addresses these challenges through a novel approach that aggregates exemplar-specific feature information across scales, resulting in high-resolution queries capable of detecting both large and small objects. This method not only surpasses state-of-the-art few-shot counters in accuracy by 10% but also operates three times faster while requiring less GPU memory. The implications of this development are profound, as it enhances the ability to analyze complex images more efficiently, paving the way for improved applications in various fields such as surveillance, wildlife monitoring, and urban planning.