CountGD++：面向开放世界计数的泛化提示
CountGD++: Generalized Prompting for Open-World Counting
把计数任务重新表述为开放词表的提示词条件定位问题：用文本或视觉示例指定目标，用负向提示词排除干扰，并在推理时自动生成伪示例，省去人工标注；这套提示词接口可直接迁移到开放词表检测与基于示例的目标定位。
原文依据 we extend the prompt to enable what not to count to be described with text and/or visual examples
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自动统计图像和视频中物体数量的方法，其灵活性和准确性受限于目标的指定方式。现有方法虽然允许用户用文本和视觉示例描述目标物体，但视觉示例必须在图像内部人工标注，而且无法指定哪些物体不应被计数。为弥补这些不足，我们引入了扩展目标指定方式的新能力。具体而言，我们扩展提示词，使其能够用文本和/或视觉示例描述哪些物体不应被计数；提出「伪示例」概念，在推理时自动完成视觉示例的标注；并扩展计数模型，使其能够接受来自自然图像和合成外部图像的视觉示例。我们还把新的计数模型 CountGD++ 用作大语言模型的视觉专家智能体。这些贡献共同扩展了多模态开放世界计数的提示灵活性，并在多个数据集上显著提升了准确性、效率和泛化能力。代码见 https://www.robots.ox.ac.uk/ vgg/research/countgd/。
英文原文摘要 1145 字符
The flexibility and accuracy of methods for automatically counting objects in images and videos are limited by the way the object can be specified. While existing methods allow users to describe the target object with text and visual examples, the visual examples must be manually annotated inside the image, and there is no way to specify what not to count. To address these gaps, we introduce novel capabilities that expand how the target object can be specified. Specifically, we extend the prompt to enable what not to count to be described with text and/or visual examples, introduce the concept of `pseudo-exemplars' that automate the annotation of visual examples at inference, and extend counting models to accept visual examples from both natural and synthetic external images. We also use our new counting model, CountGD++, as a vision expert agent for an LLM. Together, these contributions expand the prompt flexibility of multi-modal open-world counting and lead to significant improvements in accuracy, efficiency, and generalization across multiple datasets. Code is available at https://www.robots.ox.ac.uk/ vgg/research/countgd/.