Abstract
The Odeuropa Challenge on Olfactory Object Recognition aims to foster the development of object detection in the visual arts and to promote an olfactory perspective on digital heritage. Object detection in historical artworks is particularly challenging due to varying styles and artistic periods. Moreover, the task is complicated due to the particularity and historical variance of predefined target objects, which exhibit a large intra-class variance, and the long tail distribution of the dataset labels, with some objects having only very few training examples. These challenges should encourage participants to create innovative approaches using domain adaptation or few-shot learning. We provide a dataset of 2647 artworks annotated with 20 120 tightly fit bounding boxes that are split into a training and validation set (public). A test set containing 1140 artworks and 15 480 annotations is kept private for the challenge evaluation.
Abstract (translated)
嗅觉对象识别odeuropa挑战旨在促进视觉艺术中的对象检测和发展数字遗产的嗅觉视角。在历史艺术作品中,对象检测特别具有挑战性,因为风格和艺术家时期不断变化。此外,任务变得复杂,因为预先定义的目标对象的特别性和历史差异,表现出班级内的巨大差异,以及数据标签的长尾分布,其中一些对象仅有很少的训练示例。这些挑战应该鼓励参与者使用域适应或少量学习等方法创造创新性的方法。我们提供了一份包含2647件艺术品并注释了20,120个紧密贴合的边界框的数据集,并将其分为训练和验证集(公开)。一个测试集包含1140件艺术品和15,480个注释,用于挑战评估。
URL
https://arxiv.org/abs/2301.09878
