ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition
This work targets researchers in computer vision and digital heritage by creating a benchmark for olfactory object recognition in art, but it is incremental as it focuses on dataset creation and challenge formulation rather than novel methods.
The paper introduces the ODOR challenge to advance object detection in historical artworks, addressing challenges like varying artistic styles and long-tail label distributions, and provides a dataset of 2,647 artworks with 20,120 bounding boxes for training and validation.
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.