MaPPER：面向指称表达理解的多模态先验引导的参数高效微调
MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression Comprehension
AI总结：
针对指称表达理解任务，提出MaPPER框架，通过动态先验适配器和局部卷积适配器实现参数高效微调，仅用1.41%可调参数达到最优性能。
摘要：
指称表达理解（REC）旨在通过自然语言定位局部视觉区域，是一项高度依赖多模态对齐的任务。现有大多数方法利用强大的预训练模型，通过全量微调来迁移视觉/语言知识。然而，对整个骨干网络进行全量微调不仅会破坏预训练中嵌入的丰富先验知识，还会产生显著的计算成本。受近期参数高效迁移学习（PETL）方法兴起的启发，我们旨在以有效且高效的方式解决REC任务。直接将现有PETL方法应用于REC任务并不合适，因为它们缺乏精确局部视觉感知和视觉-语言对齐所需的特定领域能力。因此，我们提出了一种新颖的多模态先验引导的参数高效微调框架，即MaPPER。具体而言，MaPPER包含由对齐先验引导的动态先验适配器，以及用于提取精确局部语义以增强视觉感知的局部卷积适配器。此外，我们提出了先验引导文本模块，以进一步利用先验促进跨模态对齐。在三个广泛使用的基准上的实验结果表明，与全量微调和其他PETL方法相比，MaPPER仅使用1.41%的可调骨干参数即可实现最佳准确率。我们的代码可在https://github.com/liuting20/MaPPER获取。
英文摘要：
Referring Expression Comprehension (REC), which aims to ground a local visual region via natural language, is a task that heavily relies on multimodal alignment. Most existing methods utilize powerful pre-trained models to transfer visual/linguistic knowledge by full fine-tuning. However, full fine-tuning the entire backbone not only breaks the rich prior knowledge embedded in the pre-training, but also incurs significant computational costs. Motivated by the recent emergence of Parameter-Efficient Transfer Learning (PETL) methods, we aim to solve the REC task in an effective and efficient manner. Directly applying these PETL methods to the REC task is inappropriate, as they lack the specific-domain abilities for precise local visual perception and visual-language alignment. Therefore, we propose a novel framework of Multimodal Prior-guided Parameter Efficient Tuning, namely MaPPER. Specifically, MaPPER comprises Dynamic Prior Adapters guided by an aligned prior, and Local Convolution Adapters to extract precise local semantics for better visual perception. Moreover, the Prior-Guided Text module is proposed to further utilize the prior for facilitating the cross-modal alignment. Experimental results on three widely-used benchmarks demonstrate that MaPPER achieves the best accuracy compared to the full fine-tuning and other PETL methods with only 1.41% tunable backbone parameters. Our code is available at https://github.com/liuting20/MaPPER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
