  Recently Transformers have provided state-of-the-art performance in sparse
matching, crucial to realize high-performance 3D vision applications. Yet,
these Transformers lack efficiency due to the quadratic computational
complexity of their attention mechanism. To solve this problem, we employ an
efficient linear attention for the linear computational complexity. Then, we
propose a new attentional aggregation that achieves high accuracy by
aggregating both the global and local information from sparse keypoints. To
further improve the efficiency, we propose the joint learning of feature
matching and description. Our learning enables simpler and faster matching than
Sinkhorn, often used in matching the learned descriptors from Transformers. Our
method achieves competitive performance with only 0.84M learnable parameters
against the bigger SOTAs, SuperGlue (12M parameters) and SGMNet (30M
parameters), on three benchmarks, HPatch, ETH, and Aachen Day-Night.
