ABACUS: 自适应统一基础模型，桥接图像计数理解与生成
ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation
AI总结：
提出ABACUS统一视觉语言模型，通过密度感知自适应缩放、边界感知计数策略和循环一致性GRPO，无需特定训练即可处理多种计数任务并生成计数忠实图像，在七个基准上达到最优。
摘要：
ABACUS是一个统一的视觉语言模型，无需任何基准特定训练即可处理目标计数、人群计数、指代表达计数和计数忠实图像生成。我们的模型基于现有的3B参数统一基础模型，并通过三个关键创新适应目标定位任务：使用目标性图的密度感知自适应缩放进行空间定位；通过GRPO的边界感知计数策略消除裁剪边界误差；以及循环一致性GRPO策略，其中理解分支自我批评生成的输出，无需任何外部标注即可缩小理解-生成差距。ABACUS在七个基准上取得了最先进的结果，超越了特定任务专家和更大的通用模型。
英文摘要：
We present ABACUS, a unified vision-language model that jointly addresses object counting, crowd counting, referring-expression counting, and count-faithful image generation within a single 3B-parameter model. ABACUS introduces three contributions: density-aware adaptive zooming paired with an objectness map from multi-head self-attention decomposition to spatially ground count predictions; a boundary-aware count policy trained via GRPO with nested local, boundary, and global rewards to eliminate over- and undercounting at crop boundaries; and a cycle-consistent GRPO strategy in which the frozen understanding branch scores generated candidates on count-deviation and aesthetic quality, closing the understanding-generation synergy gap without any external critic or annotation. ABACUS achieves state-of-the-art results across seven benchmarks spanning object counting (FSC-147, CARPK), crowd counting (ShanghaiTech A/B), referring-expression counting (REC-8K), count-faithful generation (CoCoCount, T2I-CompBench, GenEval), and count reasoning (CountQA), surpassing both task-specific specialists and larger generalist models. Project page is at https://mondalanindya.github.io/pages/ABACUS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
