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

• URL: http://arxiv.org/abs/2508.00728v1
• Date: Fri, 01 Aug 2025 15:51:39 GMT
• Title: YOLO-Count: Differentiable Object Counting for Text-to-Image Generation
• Authors: Guanning Zeng, Xiang Zhang, Zirui Wang, Haiyang Xu, Zeyuan Chen, Bingnan Li, Zhuowen Tu,
• Abstract summary: YOLO-Count is a differentiable open-vocabulary object counting model that tackles both general counting challenges and enables precise quantity control for text-to-image (T2I) generation.<n>A core contribution is the 'cardinality' map, a novel regression target that accounts for variations in object size and spatial distribution.
• Score: 49.79896127854202
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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