Object counting is pivotal for understanding the composition of scenes. Previously, this task was dominated by class-specific methods, which have gradually evolved into more adaptable class-agnostic strategies. However, these strategies come with their own set of limitations, such as the need for manual exemplar input and multiple passes for multiple categories, resulting in significant inefficiencies. This paper introduces a new, more practical approach enabling simultaneous counting of multiple object categories using an open vocabulary framework. Our solution, OmniCount, stands out by using semantic and geometric insights from pre-trained models to count multiple categories of objects as specified by users, all without additional training. OmniCount distinguishes itself by generating precise object masks and leveraging point prompts via the Segment Anything Model for efficient counting. To evaluate OmniCount, we created the OmniCount-191 benchmark, a first-of-its-kind dataset with multi-label object counts, including points, bounding boxes, and VQA annotations. Our comprehensive evaluation in OmniCount-191, alongside other leading benchmarks, demonstrates OmniCount's exceptional performance, significantly outpacing existing solutions and heralding a new era in object counting technology.

翻译：目标计数对于理解场景构成至关重要。此前，该任务主要由类别特定方法主导，这些方法逐渐演变为适应性更强的类别无关策略。然而，这些策略自身存在局限性，例如需要手动示例输入和对多个类别进行多次遍历，导致效率显著低下。本文提出一种更实用的新方法，支持在开放词汇框架下同时计数多个目标类别。我们的解决方案OmniCount通过利用预训练模型中的语义和几何信息，无需额外训练即可按用户指定对多个类别目标进行计数。其独特之处在于精确生成目标掩码，并借助Segment Anything模型的点提示实现高效计数。为评估OmniCount，我们创建了OmniCount-191基准数据集——首个包含多标签目标计数（含点、边界框及VQA标注）的此类数据集。在OmniCount-191及其他主流基准上的全面评估表明，OmniCount性能卓越，显著超越现有解决方案，开创了目标计数技术的新纪元。