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This paper introduces AdaCount, a training-free framework that enhances zero-shot object counting (ZOC) by employing similarity-guided spatial and feature adaptation techniques. By generating a prototype-driven similarity map, AdaCount effectively reallocates image resolution and modulates encoder representations to focus on target instances, addressing the limitations of previous methods like SAM3 in densely populated scenes. Extensive evaluations across six counting benchmarks demonstrate that AdaCount achieves state-of-the-art performance without the need for additional training data or model retraining.
Achieving state-of-the-art zero-shot object counting without any training, AdaCount redefines how we leverage similarity maps for enhanced object detection in complex scenes.
Zero-shot object counting (ZOC) aims to count instances of arbitrary object categories specified only through textual prompts. Recent training-free approaches leverage foundation models such as SAM to reformulate counting as a prompt-driven segmentation task, eliminating the need for costly counting-specific training data with point-level annotations. More recently, SAM3 introduced promptable concept segmentation, enabling the zero-shot segmentation of all instances corresponding to a text-defined concept. However, SAM3 struggles in densely populated scenes containing numerous small objects, where limited image resolution and insufficient attention to target-relevant regions often lead to missed instances and poor instance separation, hindering accurate object counting. To address this limitation, we propose AdaCount, a training-free framework for ZOC based on similarity-guided spatial and feature adaptation. AdaCount first estimates a prototype-driven similarity map that identifies target-relevant regions. This similarity map subsequently guides two complementary adaptations: (i) similarity-guided spatial warping, which reallocates image resolution toward target instances, and (ii) feature modulation, which amplifies target-relevant encoder representations. Together, these adaptations enable SAM3 to devote greater representational capacity to target-relevant regions while preserving global image context, without requiring any model retraining. Extensive experiments across six diverse counting benchmarks establish AdaCount as a new SOTA among training-free ZOC approaches.
