@inproceedings{
cai2024prism,
title={{PRISM}: {PR}ogressive dependency maxImization for Scale-invariant image Matching},
author={Xudong Cai and Yongcai Wang and Lun Luo and Minhang Wang and Deying Li and Jintao Xu and Weihao Gu and Rui Ai},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=QZkHgKqIuG}
}
Image matching aims at identifying corresponding points between a pair of images. Currently, detector-free methods have shown impressive performance in challenging scenarios, thanks to their capability of generating dense matches and global receptive field. However, performing feature interaction and proposing matches across the entire image is unnecessary, as not all image regions contribute beneficially to the matching process. Interacting and matching in unmatchable areas can introduce errors, reducing matching accuracy and efficiency. Furthermore, the scale discrepancy issue still troubles existing methods. To address above issues, we propose PRogressive dependency maxImization for Scale-invariant image Matching (PRISM), which jointly prunes irrelevant patch features and tackles the scale discrepancy. To do this, we first present a Multi-scale Pruning Module (MPM) to adaptively prune irrelevant features by maximizing the dependency between the two feature sets. Moreover, we design the Scale-Aware Dynamic Pruning Attention (SADPA) to aggregate information from different scales via a hierarchical design. Our method's superior matching performance and generalization capability are confirmed by leading accuracy across various evaluation benchmarks and downstream tasks. The code will be publicly available.
PRISM: PRogressive dependency maxImization for Scale-invariant image Matching
Xudong Cai Yongcai Wang Lun Luo Minhang Wang Deying Li Jintao Xu Weihao Gu Rui Ai