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.

本文提出了一种名为 MatchDet 的协作框架，用于图像匹配和物体检测，通过三个新颖模块实现两个任务的协作学习，包括权重空间注意力模块（WSAM）用于检测器，加权注意力模块（WAM）和框过滤器用于匹配器。实验证明我们的方法在新的基准数据集 Warp-COCO 和 miniScanNet 上有效，并取得了竞争性的改进。