提升零样本物体计数的定量与空间意识
Boosting Quantitive and Spatial Awareness for Zero-Shot Object Counting
AI总结：
本文提出QICA框架，通过引入协同提示策略和成本聚合解码器，提升零样本物体计数的定量感知和空间聚合能力，实验表明其在多个数据集上具有优越的泛化性能。
摘要：
零样本物体计数（ZSOC）旨在通过文本描述枚举任意类别物体，而无需视觉示例。然而，现有方法常将计数视为粗略检索任务，缺乏细粒度数量感知。此外，由于模型适应过程中的特征空间扭曲，它们常表现出空间不敏感和泛化能力差。为解决这些挑战，我们提出QICA框架，结合数量感知与鲁棒空间聚合。具体而言，我们引入协同提示策略（SPS），通过数值条件提示适配视觉和语言编码器，弥合语义识别与定量推理之间的差距。为缓解特征扭曲，我们提出成本聚合解码器（CAD），直接操作视觉-文本相似性图。通过空间聚合细化这些图，CAD防止过拟合同时保持零样本可转移性。此外，多级数量对齐损失（L_MQA）用于强制整个流程的数值一致性。在FSC-147上的广泛实验表明，其性能具有竞争力，而在CARPK和ShanghaiTech-A上的零样本评估验证了其对未见领域的优越泛化能力。
英文摘要：
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 \textbf{QICA}, a novel framework that synergizes \underline{q}uantity percept\underline{i}on with robust spatial \underline{c}ast \underline{a}ggregation. Specifically, we introduce a Synergistic Prompting Strategy (\textbf{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 (\textbf{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.
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。
