reviews: 
Comments: This paper proposes a self-supervised method for image correspondence learning from easily accessible internet videos. The proposed method first trains an image-matching network on a standard dataset with GT supervision and then combines it with complementary image-matching methods to generate candidate correspondences between nearby frames of internet videos. Then, robust fitting is applied to remove outliers in the generated candidate correspondences. The remaining correspondences are used to re-train the image-matching network. By using this self-supervised training scheme, the performance improvement on existing image-matching models is impressive.
Strengths: Many of the current deep networks suffer from poor generalization ability to unknown data distributions when the amount and diversity of training data are limited. Fine-tuning the model on the target data distribution with a small amount of data from the target domain with GT supervision is a natural way. However, obtaining GT information of the data from the target domain might not always be easy, especially in correspondence matching, pose estimation, 3D reconstruction, etc. 

To address this challenge, this paper introduces a self-supervision strategy for image matchers using readily available internet videos. There is no need to run an SfM or COMAP pipeline, which is usually computationally expensive and un-robust to in-the-wild images, to obtain the GT correspondences. The performance improvement on three standard image-matching networks, diverse evaluation images, and various downstream tasks is impressive.
Weaknesses: My comments below are more like questions instead of weaknesses. 

(1) The proposed method combines a baseline image-matching network (e.g., SuperGlue, LoFTR, DKM) trained on a standard dataset and complementary image-matching methods to generate candidate correspondences. From the experiments section, the complementary image matching methods perform inferiorly than the baseline network. I have two questions here. 
     a. Since the performance is inferior, why are they needed? Will this increase the number of estimated correspondences between two images and thus improve the performance?
     b. The introduction says "multiple" complementary image matching methods are used. While in the experiments section, it seems that only one complementary image matching method is used for each baseline method (SuperGlue, LoFTR & DKM). Will the number of complementary methods affect the performance?

(2) Does batch size affect the self-supervision performance? From the implementation details, the GIM label generation on 50 hours of YouTube videos takes 4 days on 16 A100 GPUs. How long will it take to re-train a baseline network using the generated labels (and on which type and how many GPUs)? Will the proposed method still work on an RTX 3090, which is commonly used in universities?
Recommendation: 8: accept, good paper
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good