细粒度开放词汇目标检测与细粒度提示：任务、数据集与基准
Fine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark
AI总结：
本文提出3F-OVD任务，扩展细粒度监督目标检测至开放词汇场景，引入NEU-171K数据集，并提出简单有效的后处理技术。
摘要：
开放词汇检测器旨在定位和识别新类别的对象。然而，用于开放词汇学习的视觉感知语言词汇数据的差异可能导致不公平和不可靠的评估。近期评估方法尝试通过引入对象属性或添加位置和特征到描述中来解决此问题。然而，由于这些属性和位置依赖于图像的具体细节而非类别，检测器在没有通过人工标注提供的精确描述时无法做出准确预测。本文引入了3F-OVD，一种新的任务，将监督细粒度目标检测扩展到开放词汇设置。我们的任务直观且具有挑战性，需要深入理解细粒度描述并仔细关注图像中的细粒度细节，以准确检测细粒度对象。此外，由于合格的细粒度目标检测数据集稀缺，我们创建了一个新的数据集NEU-171K，适用于监督和开放词汇设置。我们在该数据集上基准测试了最先进的目标检测器，并提出了简单而有效的后处理技术。
英文摘要：
Open-vocabulary detectors are proposed to locate and recognize objects in novel classes. However, variations in vision-aware language vocabulary data used for open-vocabulary learning can lead to unfair and unreliable evaluations. Recent evaluation methods have attempted to address this issue by incorporating object properties or adding locations and characteristics to the captions. Nevertheless, since these properties and locations depend on the specific details of the images instead of classes, detectors can not make accurate predictions without precise descriptions provided through human annotation. This paper introduces 3F-OVD, a novel task that extends supervised fine-grained object detection to the open-vocabulary setting. Our task is intuitive and challenging, requiring a deep understanding of Fine-grained captions and careful attention to Fine-grained details in images in order to accurately detect Fine-grained objects. Additionally, due to the scarcity of qualified fine-grained object detection datasets, we have created a new dataset, NEU-171K, tailored for both supervised and open-vocabulary settings. We benchmark state-of-the-art object detectors on our dataset for both settings. Furthermore, we propose a simple yet effective post-processing technique. Our data, annotations and codes are available at https://github.com/tengerye/3FOVD.
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。
