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Lack of texture often causes ambiguity in matching, and handling this issueis an important challenge in optical flow estimation. Some methods insertstacked transformer modules that allow the network to use global information ofcost volume for estimation. But the global information aggregation often incursserious memory and time costs during training and inference, which hindersmodel deployment. We draw inspiration from the traditional local regionconstraint and design the local similarity aggregation (LSA) and the shiftedlocal similarity aggregation (SLSA). The aggregation for cost volume isimplemented with lightweight modules that act on the feature maps. Experimentson the final pass of Sintel show the lower cost required for our approach whilemaintaining competitive performance.

LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation