In recent years, powered by the learned discriminative representation via graph neural network (GNN) models, deep matching methods have made great progresses in task of semantic features. However, these usually rely on heuristically generated patterns, which may introduce unreliable relationships to hurt performance. this paper, we propose a joint learning and network, named GLAM, explore relia...

We tackle the essential task of finding dense visual correspondences between a pair images. This is challenging problem due to various factors such as poor texture, repetitive patterns, illumination variation, and motion blur in practical scenarios. In contrast methods that use correspondence ground-truths direct supervision for local feature matching training, we train 3DG-STFM: multi-modal mo...