AgriCountDINO：农业场景下的样本引导计数与定位框架
AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture
农业里的数穗数果问题有新解法了：只训 8.4M 参数就能一边数一边标出每个目标位置，零样本跨类别表现还比专门方法好 6%。
AgriCountDINO 是一个面向农业的样本引导计数与定位框架，基于冻结的 DINOv3 多尺度特征，仅用 8.4M 可训练参数（约为 TasselNetV4 的十分之一）。它通过样本框指定目标，无需针对类别重新训练。在 TPC-268 基准上，三样本 MAE 达 11.92，比对比方法降低 9.7% 计数误差。仅在 TPC-268 上训练的模型，在 FSC-147 未见类别上零样本 MAE 为 14.25，比最优零样本方法提升 6.0%。
AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture
Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary widely across species and imaging conditions. Exemplar boxes specify the target without category-specific retraining, and point predictions identify the individual instances contributing to the count. We introduce AgriCountDINO, a parameter-efficient exemplar-guided framework for joint counting and localization. It conditions frozen multiscale DINOv3 features on exemplar appearance and size, then progressively decodes them into target points. Missed-object recovery extends supervision to targets overlooked by initial matching, and exemplar-adaptive point NMS filters duplicate predictions according to exemplar scale. With 8.4M trainable parameters, approximately one-tenth of TasselNetV4's, AgriCountDINO achieves a three-shot MAE of 11.92 on the TPC-268 benchmark, reducing counting error by 9.7\% while providing individual target locations. Trained only on TPC-268, it achieves a zero-shot MAE of 14.25 on unseen generic object categories in FSC-147, improving upon the best compared zero-shot method by 6.0\% without target-domain training or fine-tuning.
