In recent years, deep learning has made significant progress in the field of image matching, but model generalization remains a challenge. In order to solve this problem, researchers from Xiamen University, Intel and DJI proposed a new method: GIM (Learning Generalizable Image Matcher from Internet Videos). This research aims to improve the generalization ability of image matching models so that they can better adapt to various scenarios and data. GIM uses Internet videos for training and proposes the Zero-shot Evaluation Benchmark (ZEB) for the first time to evaluate the generalization performance of the model. This method is expected to significantly improve the practicality and reliability of image matching technology and bring new breakthroughs to the field of computer vision.
Image matching is a basic task of computer vision. In recent years, matching models based on deep learning have become increasingly popular. In order to solve the problem of generalization of deep learning methods, researchers from Xiamen University, Intel, and DJI proposed GIM: Learning Generalizable Image Matcher from Internet Videos. GIM allows matching models to learn strong generalization capabilities from Internet videos and is suitable for training all matching models. The author proposed the first Zero-shot Evaluation Benchmark (ZEB). The evaluation results show that GIM can significantly improve the generalization performance of the matching model.
The emergence of GIM provides new ideas for improving the generalization ability of image matching models, and its excellent performance on the Zero-shot Evaluation Benchmark also proves its effectiveness. This research result is of great significance in promoting the progress and application of image matching technology and deserves further attention and research.