统一检测与定位，让遥感图像理解更准更快
MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote Sensing
- 多分支结构融合空间、视觉和类别特征生成精准候选框
- 在OPT-RSVG和DIOR-RSVG上超越现有方法，保持传统检测能力
- 适合需要高精度目标定位的遥感分析场景
我们提出一种统一框架，将遥感图像中的目标检测（OD）与视觉定位（VG）任务整合。为支持传统检测并建立视觉定位的直观先验，我们使用指代表达数据微调一个开放集目标检测器，将其视为部分监督的检测任务。第一阶段构建图像的图表示，包含对象查询、类别嵌入和候选位置。随后，任务感知架构处理该图以完成视觉定位任务。模型包括：(i) 多分支网络，融合空间、视觉和类别特征生成任务感知候选框；(ii) 对象推理网络，对候选框分配概率，并通过软选择机制实现最终指代对象定位。在OPT-RSVG和DIOR-RSVG数据集上，模型表现优于现有最先进方法，同时保留经典目标检测能力。代码将在仓库中公开： https://github.com/rd20karim/MB-ORES。
原文摘要 · Abstract (English)
We propose a unified framework that integrates object detection (OD) and visual grounding (VG) for remote sensing (RS) imagery. To support conventional OD and establish an intuitive prior for VG task, we fine-tune an open-set object detector using referring expression data, framing it as a partially supervised OD task. In the first stage, we construct a graph representation of each image, comprising object queries, class embeddings, and proposal locations. Then, our task-aware architecture processes this graph to perform the VG task. The model consists of: (i) a multi-branch network that integrates spatial, visual, and categorical features to generate task-aware proposals, and (ii) an object reasoning network that assigns probabilities across proposals, followed by a soft selection mechanism for final referring object localization. Our model demonstrates superior performance on the OPT-RSVG and DIOR-RSVG datasets, achieving significant improvements over state-of-the-art methods while retaining classical OD capabilities. The code will be available in our repository: \url{https://github.com/rd20karim/MB-ORES}.
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