  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.
