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

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Zero-shot object counting aims to estimate the number of objects specified by a text query without category-specific training. Recent approaches primarily rely on density regression or detection-style instance prediction. While effective, density-based models often suffer from spatial ambiguity and background leakage due to weakly regulated mass allocation, leading to fragmented or part-biased representations that increase counting error in complex scenes. In this work, we propose an instance-aware dual-decoder framework that structurally couples density and point representations for zero-shot

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