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。

MatchDet：图像匹配与目标检测的协同框架

MatchDet: A Collaborative Framework for Image Matching and Object  Detection

Numerical validation is at the core of machine learning research as it allows
to assess the actual impact of new methods, and to confirm the agreement
between theory and practice. Yet, the rapid development of the field poses
several challenges: researchers are confronted with a profusion of methods to
compare, limited transparency and consensus on best practices, as well as
tedious re-implementation work. As a result, validation is often very partial,
which can lead to wrong conclusions that slow down the progress of research. We
propose Benchopt, a collaborative framework to automate, reproduce and publish
optimization benchmarks in machine learning across programming languages and
hardware architectures. Benchopt simplifies benchmarking for the community by
providing an off-the-shelf tool for running, sharing and extending experiments.
To demonstrate its broad usability, we showcase benchmarks on three standard
learning tasks: $\ell_2$-regularized logistic regression, Lasso, and ResNet18
training for image classification. These benchmarks highlight key practical
findings that give a more nuanced view of the state-of-the-art for these
problems, showing that for practical evaluation, the devil is in the details.
We hope that Benchopt will foster collaborative work in the community hence
improving the reproducibility of research findings.

提出了 Benchopt，这是一个协作框架，旨在自动化、再现和发布跨编程语言和硬件架构的机器学习优化基准测试，为社区提供了一个现成的工具，以简化基准测试，并展示了三个标准学习任务的基准测试结果，重点关注实际评估中的细节问题，以此促进社区协作工作，改善研究结果的可再生性。