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Forecasting future trajectories of agents in complex traffic scenes requiresreliable and efficient predictions for all agents in the scene. However,existing methods for trajectory prediction are either inefficient or sacrificeaccuracy. To address this challenge, we propose ADAPT, a novel approach forjointly predicting the trajectories of all agents in the scene with dynamicweight learning. Our approach outperforms state-of-the-art methods in bothsingle-agent and multi-agent settings on the Argoverse and Interactiondatasets, with a fraction of their computational overhead. We attribute theimprovement in our performance: first, to the adaptive head augmenting themodel capacity without increasing the model size; second, to our design choicesin the endpoint-conditioned prediction, reinforced by gradient stopping. Ouranalyses show that ADAPT can focus on each agent with adaptive prediction,allowing for accurate predictions efficiently. https://KUIS-AI.github.io/adapt

ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation