Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this paper, a collaborative framework called MatchDet (i.e. task-collaborative) is proposed for image matching and object detection to obtain mutual improvements. To achieve the collaborative learning of the two tasks, we propose three novel modules, including a Weighted Spatial Attention Module (WSAM) for Detector, and Weighted Attention Module (WAM) and Box Filter for Matcher. Specifically, the WSAM highlights the foreground regions of target image to benefit the subsequent detector, the WAM enhances the connection between the foreground regions of pair images to ensure high-quality matches, and Box Filter mitigates the impact of false matches. We evaluate the approaches on a new benchmark with two datasets called Warp-COCO and miniScanNet. Experimental results show our approaches are effective and achieve competitive improvements.

翻译：图像匹配与目标检测是两项基础且具有挑战性的任务，而许多相关应用将它们视为独立任务（即任务分离）。本文提出一种名为MatchDet的协同框架（即任务协同），用于图像匹配与目标检测，以实现两者的相互提升。为实现两项任务的协同学习，我们提出了三个新型模块，包括用于检测器的加权空间注意力模块（WSAM），以及用于匹配器的加权注意力模块（WAM）和框筛选器。具体而言，WSAM突出目标图像的前景区域以辅助后续检测器，WAM增强配对图像前景区域间的关联以确保高质量的匹配，而框筛选器则减轻误匹配的影响。我们在包含Warp-COCO和miniScanNet两个数据集的新基准上评估该方法。实验结果表明，我们的方法有效且取得了具有竞争力的改进效果。