In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object’s label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins.
Gromit-MPX is an on-screen annotation tool that works with any Unix desktop environment under X11 as well as Wayland. - GitHub - bk138/gromit-mpx: Gromit-MPX is an on-screen annotation tool that works with any Unix desktop environment under X11 as well as Wayland.
@SafeVarargs
Is a cure for the warning: [unchecked] Possible heap pollution from parameterized vararg type Foo.
Is part of the method's contract, hence why the annotation has runtime retention.
Is a promise to the caller of the method that the method will not mess up the heap using the generic varargs argument.
T. Tran, N. Tran, A. Teka Hadgu, and R. Jäschke. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (September 2015)
T. Tran, N. Tran, A. Hadgu, and R. Jäschke. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 97--106. Association for Computational Linguistics, (September 2015)
J. Jeon, V. Lavrenko, and R. Manmatha. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, page 119--126. New York, NY, USA, ACM, (2003)
W. Wu, B. Zhang, and M. Ostendorf. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, page 689--692. Stroudsburg, PA, USA, Association for Computational Linguistics, (2010)
S. Repp, S. Linckels, and C. Meinel. Proceedings of the international workshop on Educational multimedia and multimedia education, page 19--26. New York, NY, USA, ACM, (2007)
A. Stent, and A. Loui. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, page 59--65. New York, NY, USA, ACM, (2001)
P. Chirita, S. Costache, W. Nejdl, and S. Handschuh. WWW '07: Proceedings of the 16th International Conference on World Wide Web, page 845--854. New York, NY, USA, ACM, (2007)
S. Pyysalo, F. Ginter, K. Haverinen, J. Heimonen, T. Salakoski, and V. Laippala. Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, page 25--32. Stroudsburg, PA, USA, Association for Computational Linguistics, (2007)
A. Russo, and D. Peacock. Archives & Museum Informatics: Museums and the Web 2009, (2009)Under Creative Commons License: Attribution Non-Commercial No Derivatives.
C. Marshall. Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems, page 40--49. New York, NY, USA, ACM, (1998)
X. Wu, L. Zhang, and Y. Yu. WWW '06: Proceedings of the 15th international conference on World Wide Web, page 417--426. New York, NY, USA, ACM Press, (2006)
J. Schnasse, V. Heydegger, and E. Weiper. The eXtensible Chacterisation Languages -- XCL, volume 3 of Kölner Beiträge zu einer geisteswissenschaftlichen Fachinformatik, chapter 3, Verlag Dr. Kovac, Hamburg, (2009)
J. Tang, M. Hong, J. Li, and B. Liang. International Semantic Web Conference, volume 4273 of Lecture Notes in Computer Science, page 640-653. Springer, (2006)
J. Lowe, C. Baker, and C. Fillmore. Proceedings of ACL SIGLEX Workshop on Tagging Text with Lexical Semantics, page 18--24. Washington, D.C., ACL, (1997)
V. Tanasescu, and O. Streibel. Proceedings of the International Workshop on Emergent Semantics and Ontology Evolution (ESOE2007) at ISWC/ASWC2007, Busan, South Korea, (November 2007)
M. Dowman, V. Tablan, H. Cunningham, and B. Popov. Proceedings of the 14th International World Wide Web Conference, Chiba, Japan, (2005)http://gate.ac.uk/sale/www05/web-assisted-annotation.pdf
http://prestospace.org/training/images/WWW05.pdf.
P. Cimiano, P. Haase, M. Herold, M. Mantel, and P. Buitelaar. Proceedings of OntoLex - From Text to Knowledge: The Lexicon/Ontology Interface (Workshop at the International Semantic Web Conference), (2007)
B. van Elst; Andreas Dengel. KI 2009: Advances in Artificial Intelligence. Künstliche Intelligenz (KI-2009), September 15-18, Paderborn, Germany, volume 5803 of Lecture Notes in Artificial Intelligence, LNAI, page 249-256. Springer-Verlag, Heidelberg, (September 2009)
R. Kawase, E. Herder, and W. Nejdl. Learning in the Synergy of Multiple Disciplines, Proceedings of the EC-TEL 2009, volume 5794 of Lecture Notes in Computer Science, Berlin/Heidelberg, Springer, (October 2009)
B. Kettler, J. Starz, W. Miller, and P. Haglich. International Semantic Web Conference, volume 3729 of Lecture Notes in Computer Science, page 446-460. Springer, (2005)