Forecast-MAE: マスク付きオートエンコーダによる運動予測の自己教師あり事前学習
Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders
エージェントの軌跡と道路網の相互関係を活かしたマスキング戦略で、運動予測タスクを自己教師あり学習する手法を提案し、Argoverse 2で競争力のある性能を示した。
著者: Jie Cheng, Xiaodong Mei, Ming Liu
分類: cs.RO, cs.CV
原文アブストラクト
This study explores the application of self-supervised learning (SSL) to the task of motion forecasting, an area that has not yet been extensively investigated despite the widespread success of SSL in computer vision and natural language processing. To address this gap, we introduce Forecast-MAE, an extension of the mask autoencoders framework that is specifically designed for self-supervised learning of the motion forecasting task. Our approach includes a novel masking strategy that leverages the strong interconnections between agents' trajectories and road networks, involving complementary masking of agents' future or history trajectories and random masking of lane segments. Our experiments on the challenging Argoverse 2 motion forecasting benchmark show that Forecast-MAE, which utilizes standard Transformer blocks with minimal inductive bias, achieves competitive performance compared to state-of-the-art methods that rely on supervised learning and sophisticated designs. Moreover, it outperforms the previous self-supervised learning method by a significant margin. Code is available at https://github.com/jchengai/forecast-mae.
