Summary of Forecast-peft: Parameter-efficient Fine-tuning For Pre-trained Motion Forecasting Models, by Jifeng Wang et al.
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models
by Jifeng Wang, Kaouther Messaoud, Yuejiang Liu, Juergen Gall, Alexandre Alahi
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Forecast-PEFT, a novel fine-tuning strategy for motion forecasting that leverages the alignment between pre-training tasks and downstream tasks. By freezing most model parameters and focusing adjustments on newly introduced prompts and adapters, Forecast-PEFT preserves pre-learned representations while reducing retraining requirements. This approach enhances efficiency and ensures robust performance across datasets without extensive retraining. The authors demonstrate Forecast-PEFT’s effectiveness in motion prediction tasks, achieving higher accuracy with only 17% of the trainable parameters typically required. Additionally, the paper presents Forecast-FT, a comprehensive adaptation method that further improves prediction performance by up to 9.6% over conventional baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a better way to train models for motion forecasting. Right now, people are using a technique called self-supervised pre-training, but it’s not very efficient because it requires a lot of retraining. The authors came up with a new approach that freezes most of the model and only adjusts a few parts that need changing. This makes training faster and more accurate. They tested this approach on different datasets and found it worked really well, even better than other methods that require more retraining. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Self supervised
