CountEx通过提示词精准排除相似干扰物，实现细粒度计数
CountEx: Fine-Grained Counting via Exemplars and Exclusion
- 用语言+图像示例表达要计数和要忽略的目标
- 在97类物体对上比现有方法提升显著，新基准含1万帧标注
- 适合需要精准排除干扰的视觉计数场景
本文提出CountEx，一种判别式视觉计数框架，旨在解决现有提示方法无法显式排除视觉相似干扰物的问题。当前方法仅支持通过包含提示指定计数目标，但在杂乱场景中面对易混淆类别时易产生歧义和误计数。CountEx允许用户通过自然语言描述和可选的视觉示例，同时表达包含与排除意图。其核心是判别性查询精炼模块，通过先识别共享视觉特征，再分离排除特异性模式，最后实施选择性抑制来优化计数查询。为系统评估细粒度计数方法，我们构建了CoCount基准，包含1,780段视频和10,086个标注帧，覆盖97个类别对。实验表明，CountEx在已知与新类别物体计数任务上均显著优于现有先进方法。代码与数据已公开于https://github.com/bbvisual/CountEx。
原文摘要 · Abstract (English)
This paper presents CountEx, a discriminative visual counting framework designed to address a key limitation of existing prompt-based methods: the inability to explicitly exclude visually similar distractors. While current approaches allow users to specify what to count via inclusion prompts, they often struggle in cluttered scenes with confusable object categories, leading to ambiguity and overcounting. CountEx enables users to express both inclusion and exclusion intent, specifying what to count and what to ignore, through multimodal prompts including natural language descriptions and optional visual exemplars. At the core of CountEx is a novel Discriminative Query Refinement module, which jointly reasons over inclusion and exclusion cues by first identifying shared visual features, then isolating exclusion-specific patterns, and finally applying selective suppression to refine the counting query. To support systematic evaluation of fine-grained counting methods, we introduce CoCount, a benchmark comprising 1,780 videos and 10,086 annotated frames across 97 category pairs. Experiments show that CountEx achieves substantial improvements over state-of-the-art methods for counting objects from both known and novel categories. The data and code are available at https://github.com/bbvisual/CountEx.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。