English(EN) Dynamic Query as Budgeting for Tiny Object Detection

新的BUTR方法通过动态查询预算优化小型目标检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一种名为BUTR（基于预算的DETR）的新方法，以改进图像中的小型目标检测。该方法将查询选择和查询数量的影响解耦，专注于将动态查询作为一种“预算”形式，根据目标密度分配容量。BUTR利用一个轻量级的、非学习的预算器来估计查询需求，避免了复杂的预测器和启发式过滤。该系统在AI-TOD-v2和VisDrone等数据集上表现强劲，在保持NVIDIA T4 GPU上高效的FPS的同时，实现了高AP分数。 AI

影响这项研究可能带来更高效、更准确的目标检测系统，尤其适用于图像中小或密集排列的目标。

排序理由该集群包含一篇详细介绍计算机视觉新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Yi Zhang, Di Xiong, Ge Gao, Xiangyue Zhang, Yihang Qiu, Yuxuan Zhou, Shuo Chen · 2026-10-27 04:00

动态查询作为小型目标检测的预算

arXiv:2610.33430v1 Announce Type: new Abstract: Recent DETR-based tiny object detectors adopt dynamic query mechanisms to handle severe density imbalance. However, existing designs entangle two effects: how many and which queries are decoded. This entanglement obscures what drive…

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动态查询作为小型目标检测的预算

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