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arXiv · 2508.00728

YOLO-Count: Differentiable Object Counting for Text-to-Image Generation

Guanning Zeng·Xiang Zhang·Zirui Wang·Haiyang Xu·Zeyuan Chen·Bingnan Li·Zhuowen Tu
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Abstract

We propose YOLO-Count, a differentiable open-vocabulary object counting model that tackles both general counting challenges and enables precise quantity control for text-to-image (T2I) generation. A core contribution is the 'cardinality' map, a novel regression target that accounts for variations in object size and spatial distribution. Leveraging representation alignment and a hybrid strong-weak supervision scheme, YOLO-Count bridges the gap between open-vocabulary counting and T2I generation control. Its fully differentiable architecture facilitates gradient-based optimization, enabling accurate object count estimation and fine-grained guidance for generative models. Extensive experiments demonstrate that YOLO-Count achieves state-of-the-art counting accuracy while providing robust and effective quantity control for T2I systems.

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Guanning Zeng, Xiang Zhang, Zirui Wang, Haiyang Xu, Zeyuan Chen, Bingnan Li, Zhuowen Tu. 2025-08-01. YOLO-Count: Differentiable Object Counting for Text-to-Image Generation. https://arxiv.org/abs/2508.00728

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