提升零样本目标计数的数量与空间感知能力
Boosting Quantitive and Spatial Awareness for Zero-Shot Object Counting
提出数值条件提示和代价聚合解码器，将计数从粗检索转为细粒度数量感知，可直接用于开放词表计数和视觉语言模型的定量推理增强。
原文依据 we present QICA, a novel framework that synergizes quantity perception with robust spatial cast aggregation.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论零样本目标计数（ZSOC）旨在枚举由文本描述指定的任意类别对象，无需视觉示例。然而，现有方法通常将计数视为粗粒度的检索任务，缺乏细粒度的数量感知。此外，由于模型适应过程中的特征空间扭曲，它们经常表现出空间不敏感和泛化能力下降的问题。为了解决这些挑战，我们提出了QICA，一种新颖的框架，将数量感知与鲁棒的空间代价聚合相结合。具体而言，我们引入了一种协同提示策略（SPS），通过数值条件提示自适应视觉和语言编码器，弥合语义识别与定量推理之间的差距。为了减轻特征扭曲，我们提出了一种代价聚合解码器（CAD），直接作用于视觉-文本相似度图。通过空间聚合细化这些图，CAD防止过拟合，同时保持零样本迁移能力。此外，采用多级数量对齐损失（L_MQA）来强制整个流程中的数值一致性。在FSC-147上的大量实验证明了具有竞争力的性能，而在CARPK和ShanghaiTech-A上的零样本评估验证了对未见域的优越泛化能力。
英文原文摘要 1361 字符
Zero-shot object counting (ZSOC) aims to enumerate objects of arbitrary categories specified by text descriptions without requiring visual exemplars. However, existing methods often treat counting as a coarse retrieval task, suffering from a lack of fine-grained quantity awareness. Furthermore, they frequently exhibit spatial insensitivity and degraded generalization due to feature space distortion during model adaptation. To address these challenges, we present QICA, a novel framework that synergizes quantity perception with robust spatial cast aggregation. Specifically, we introduce a Synergistic Prompting Strategy (SPS) that adapts vision and language encoders through numerically conditioned prompts, bridging the gap between semantic recognition and quantitative reasoning. To mitigate feature distortion, we propose a Cost Aggregation Decoder (CAD) that operates directly on vision-text similarity maps. By refining these maps through spatial aggregation, CAD prevents overfitting while preserving zero-shot transferability. Additionally, a multi-level quantity alignment loss (\mathcal L _ MQA ) is employed to enforce numerical consistency across the entire pipeline. Extensive experiments on FSC-147 demonstrate competitive performance, while zero-shot evaluation on CARPK and ShanghaiTech-A validates superior generalization to unseen domains.