aiqbench analysis
This paper is squarely about machine learning: it proposes a parameter-efficient fine-tuning method (prompts and adapters) for pre-trained motion forecasting models used in autonomous driving and robotics. The headline claim, matching accuracy with about 17% of the trainable parameters, is a useful efficiency result, but it is reported on the authors' benchmarks and the 9.6% gain over baselines should be read against how strong those baselines are. For readers, the takeaway is a practical transfer-learning technique rather than a new foundation model.
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models
arXiv:2407.19564v2 Announce Type: replace-cross Abstract: Recent advancements in motion forecasting have been significantly accelerated by self-supervised pre-training techniques. However, current methods typically rely on extensive fine-tuning of pre-trained models for downstream tasks like trajectory prediction, a process that can be inefficient. This inefficiency primarily arises because traditional fine-tuning strategies fail to fully exploit the close alignment between motion prediction tasks and masked reconstruction objectives used during pre-training. To overcome these limitations, we introduce Forecast-PEFT, a parameter-efficient fine-tuning framework specifically designed for motion forecasting. Forecast-PEFT preserves most of the pre-trained model's parameters, introducing minimal yet effective adjustments through novel prompts and adapters. By doing so, Forecast-PEFT significantly reduces the number of parameters needing retraining while preserving essential pre-trained knowledge. Our method demonstrates robust generalization across multiple datasets, achieving higher accuracy with only about 17% of the parameters required by traditional full fine-tuning approaches. Additionally, our fully fine-tuned variant, Forecast-FT, further enhances prediction performance, achieving improvements of up to 9.6% over conventional baseline methods. These findings highlight Forecast-PEFT as a scalable, efficient, and effective alternative for fine-tuning pre-trained motion forecasting models. Code is available at https://github.com/csjfwang/Forecast-PEFT.
Abstract:Recent advancements in motion forecasting have been significantly accelerated by self-supervised pre-training techniques. However, current methods typically rely on extensive fine-tuning of pre-trained models for downstream tasks like trajectory prediction, a process that can be inefficient. This inefficiency primarily arises because traditional fine-tuning strategies fail to fully exploit the close alignment between motion prediction tasks and masked reconstruction objectives used during pre-training. To overcome these limitations, we introduce Forecast-PEFT, a parameter-efficient fine-tuning framework specifically designed for motion forecasting. Forecast-PEFT preserves most of the pre-trained model's parameters, introducing minimal yet effective adjustments through novel prompts and adapters. By doing so, Forecast-PEFT significantly reduces the number of parameters needing retraining while preserving essential pre-trained knowledge. Our method demonstrates robust generalization across multiple datasets, achieving higher accuracy with only about 17% of the parameters required by traditional full fine-tuning approaches. Additionally, our fully fine-tuned variant, Forecast-FT, further enhances prediction performance, achieving improvements of up to 9.6% over conventional baseline methods. These findings highlight Forecast-PEFT as a scalable, efficient, and effective alternative for fine-tuning pre-trained motion forecasting models. Code is available at this https URL.
Accepted for publication in IEEE Transactions on Intelligent Transportation Systems. This version is the accepted manuscript
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2407.19564 [cs.CV]
(or arXiv:2407.19564v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2407.19564 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/TITS.2026.3745364 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 307 words