Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting (2022-11-06T00:00:00.000000Z)

TL;DR

This work proposes a transfer learning approach for efficiently adapting pre-trained forecasting models to new domains, such as unseen agent types and scene contexts, and introduces two components that exploit prior knowledge of motion style shifts, including a low-rank motion style adapter and a modular adapter strategy.

Abstract

Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting pre-trained forecasting models to new domains, such as unseen agent types and scene contexts. Unlike the conventional fine-tuning approach that updates the whole encoder, our main idea is to reduce the amount of tunable parameters that can precisely account for the target domain-specific motion style. To this end, we introduce two components that exploit our prior knowledge of motion style shifts: (i) a low-rank motion style adapter that projects and adjusts the style features at a low-dimensional bottleneck; and (ii) a modular adapter strategy that disentangles the features of scene context and motion history to facilitate a fine-grained choice of adaptation layers. Through extensive experimentation, we show that our proposed adapter design, coined MoSA, outperforms prior methods on several forecasting benchmarks.

Authors

Alexandre Alahi

17 papers

Yuejiang Liu

4 papers

Parth Kothari

2 papers

TL;DR

Abstract

Authors

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