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GEAR

model1 events
papersTODAY 04:00 UTC

GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction

A new arXiv paper introduces GEAR, a method for predicting human trajectories that must account for both how individuals move and how agents influence one another. The authors note that prior work has relied on attention mechanisms, graph structures, and temporal encoders to model these dynamics, and propose shifting the emphasis from dynamic encoding to dynamic activation. The work appears as a cross-listing on arXiv's cs.AI category.

arxiv/trajectory-prediction/attention/research