Accurate feature matching between image pairs is fundamental for various computer vision applications. In detector-base process, the feature matcher aims to find the optimal feature correspondences, and the match filter is used for further removing mismatches. However, their connection is rarely exploited since they are usually treated as two separate issues in previous method, which may lead to suboptimal results. In this paper, we propose an end-to-end collaborative feature matching (CFM) method, which contains a keypoint learning (KL) module and a correspondence learning (CL) module, to bridge the gap between two types of works. The former improves the discrimination of keypoints, and provides high-quality dynamic matches for CL module. The latter further captures the rich context of matches, and gives effective feedback to KL module. These two modules can reinforce each other in a progressive manner. Besides, we develop an efficient version of CFM, named ECFM, using an adaptive sampling strategy to avoid the negative influence of uninformative keypoints. Experimental results indicate that both methods outperform the state-of-the-art competitors in the tasks of relative pose estimation and visual localization.