# MatchDet: A Collaborative Framework for Image Matching and Object Detection

Source: [MatchDet: A Collaborative Framework for Image Matching and Object Detection](<https://www.sota2.com/research/paper/68fa74211e209131cea5fd50>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:02:53.994Z

Authors: Jinxiang Lai, Wenlong Wu, Bin-Bin Gao, Jun Liu, Jiawei Zhan, Congchong Nie, Yi Zeng, Chengjie Wang

Published: 2023-12-18T07:11:45.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.10983>)

[PDF](<https://arxiv.org/pdf/2312.10983.pdf>)