基于Grounded预训练开集检测的视觉提示探索
Exploration of visual prompt in Grounded pre-trained open-set detection
AI总结：
针对现有文本提示方法泛化到新类别需人工反馈、易出错的问题，提出基于统计提示构建模块和任务相似性字典的视觉提示方法，在ODinW数据集上性能优于现有方法且组合推理更稳定。
摘要：
文本提示对于将预训练开集目标检测模型泛化到新类别至关重要。然而，当前的文本提示方法存在局限性：泛化到新类别时需要人工反馈，这限制了它们对复杂场景的建模能力，往往导致错误的检测结果。为解决这一局限，我们提出一种新颖的视觉提示方法，该方法从少量标注图像中学习新类别知识，将预训练检测模型泛化到新类别。为使视觉提示充分表征新类别，我们提出一个基于统计的提示构建模块，它不受预定义词汇长度的限制，从而允许在表征类别时使用更多向量。我们进一步利用预训练数据集中的类别字典设计任务特定的相似性字典，使视觉提示更具判别性。我们在ODinW数据集上评估了该方法，结果表明其性能优于现有提示学习方法，且在组合推理中表现更稳定。
英文摘要：
Text prompts are crucial for generalizing pre-trained open-set object detection models to new categories. However, current methods for text prompts are limited as they require manual feedback when generalizing to new categories, which restricts their ability to model complex scenes, often leading to incorrect detection results. To address this limitation, we propose a novel visual prompt method that learns new category knowledge from a few labeled images, which generalizes the pre-trained detection model to the new category. To allow visual prompts to represent new categories adequately, we propose a statistical-based prompt construction module that is not limited by predefined vocabulary lengths, thus allowing more vectors to be used when representing categories. We further utilize the category dictionaries in the pre-training dataset to design task-specific similarity dictionaries, which make visual prompts more discriminative. We evaluate the method on the ODinW dataset and show that it outperforms existing prompt learning methods and performs more consistently in combinatorial inference.
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。
