Abstract
AbstractThe use of deep learning makes it possible to achieve extraordinary results in all kinds of tasks related to computer vision. However, this performance is strongly related to the availability of training data and its relationship with the distribution in the eventual application scenario. This question is of vital importance in areas such as robotics, where the targeted environment data are barely available in advance. In this context, domain adaptation (DA) techniques are especially important to building models that deal with new data for which the corresponding label is not available. To promote further research in DA techniques applied to robotics, this work presents Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation), an assortment of seven datasets for the classification of kitchen utensils—a task of relevance in home-assistance robotics and a suitable showcase for DA. Along with the data, we provide a broad description of the main characteristics of the dataset, as well as a baseline using the well-known domain-adversarial training of neural networks approach. The results show the challenge posed by DA on these types of tasks, pointing to the need for new approaches in future work.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition
Reference36 articles.
1. Arbelaez P, Maire M, Fowlkes C et al (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916
2. Bolte JA, Kamp M, Breuer A, et al (2019) Unsupervised domain adaptation to improve image segmentation quality both in the source and target domain. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
3. Bousmalis K, Trigeorgis G, Silberman N et al (2016) Domain separation networks. Adv Neural Inf Process Syst 29:343–351
4. Bousmalis K, Silberman N, Dohan D, et al (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3722–3731
5. Castellanos FJ, Gallego AJ, Calvo-Zaragoza J (2020) Automatic scale estimation for music score images. Expert Syst Appl 158(113):590
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献