Boosting Quantitive and Spatial Awareness for Zero-Shot Object Counting

• URL: http://arxiv.org/abs/2603.16129v1
• Date: Tue, 17 Mar 2026 05:32:09 GMT
• Title: Boosting Quantitive and Spatial Awareness for Zero-Shot Object Counting
• Abstract summary: Zero-shot object counting (ZSOC) aims to enumerate objects of arbitrary categories specified by text descriptions without requiring visual exemplars.<n>Existing methods often treat counting as a coarse retrieval task, suffering from a lack of fine-grained quantity awareness.<n>We present textbfQICA, a novel framework that synergizes underlinequantity perceptunderlineion with robust spatial underlinecast underlineaggregation.
• Score: 21.127727991498244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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