English(EN) Comparative study of adapting pre-trained models for driving behavior video captioning

研究比较了用于驾驶视频字幕生成的LLM适应方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

一项比较研究探索了各种微调和提示方法，用于将预训练的大型语言模型（LLM）适应于自动驾驶视频字幕生成领域。研究重点是将这些技术应用于使用BDD-X数据集的SpaceTimeGPT模型。结果表明，完全微调框架在自动指标上取得了强劲的性能，有时甚至超过了基线模型，而VideoLLaVA模型上的低秩适应（LoRA）和提示工程则显示出局限性。 AI

影响这项研究可以为开发更有效的LLM适应技术，以用于自动驾驶等专业领域提供信息。

排序理由该集群包含一篇详细介绍模型适应方法比较研究的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Sayak Mallick, Philipp Geiger, Augustin Kelava · 2026-10-01 04:00

预训练模型在驾驶行为视频字幕生成任务上适应性的比较研究

arXiv:2609.39542v1 Announce Type: new Abstract: This report examines and compares some of the many fine tuning and prompting methods existing, applying them within the domain of autonomous driving. The idea is to compare these methods by adapting a Large Language Model (LLM) on a…

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预训练模型在驾驶行为视频字幕生成任务上适应性的比较研究

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