DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting

Zero-shot object counting aims to estimate the number ofobjects specified by a text query without category-specific training. Re-cent approaches primarily rely on density regression or detection-styleinstance prediction. While effective, density-based models often sufferfrom spatial ambiguity and background leakage due to weakly regulatedmass allocation, leading to fragmented or part-biased representationsthat increase counting error in complex scenes. In this work, we proposean instance-aware dual-decoder framework that structurally couples den-sity and point representations for zero-shot object counting. Instead oftreating density estimation as independent pixel-wise regression, we in-terpret it as a structured mass allocation problem over a latent set ofobject instances. Predicted instance centers induce a soft instance-wisedecomposition of the density map, upon which we enforce two geometricconstraints: (1) per-instance mass conservation, ensuring each object con-tributes approximately one unit of density mass, and (2) center-of-massalignment, encouraging each density component to concentrate aroundits corresponding predicted center. These constraints introduce instance-level geometric consistency and lead to more accurate mass allocation,thereby reducing counting error. Extensive experiments on FSC-147,PUCPR+, and CARPK show that our approach consistently reducescounting error and establishes new state-of-the-art performance in zero-shot object counting.