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.
J. Davis, и D. Huttenlocher. CSCL '95: The first international conference on Computer support for collaborative learning, стр. 84--88. Mahwah, NJ, USA, Lawrence Erlbaum Associates, Inc., (1995)
P. Dmitriev, N. Eiron, M. Fontoura, и E. Shekita. Proceedings of the 15th International Conference on World Wide Web, стр. 811--817. New York, NY, USA, ACM, (2006)
C. Neuwirth, D. Kaufer, R. Chandhok, и J. Morris. Proceedings of the 1990 ACM conference on Computer-supported cooperative work, стр. 183--195. New York, NY, USA, ACM, (1990)
C. Liao, F. Guimbreti&\#232;re, и K. Hinckley. UIST '05: Proceedings of the 18th annual ACM symposium on User interface software and technology, стр. 241--244. New York, NY, USA, ACM, (2005)
S. Bao, G. Xue, X. Wu, Y. Yu, B. Fei, и Z. Su. Proceedings of the 16th International Conference on World Wide Web, стр. 501--510. New York, NY, USA, ACM, (2007)
R. Yan, A. Natsev, и M. Campbell. MS '07: Workshop on multimedia information retrieval on The many faces of multimedia semantics, стр. 13--20. New York, NY, USA, ACM Press, (2007)
P. Dmitriev, N. Eiron, M. Fontoura, и E. Shekita. WWW '06: Proceedings of the 15th international conference on World Wide Web, стр. 811--817. New York, NY, USA, ACM, (2006)
J. Kim, R. Farzan, и P. Brusilovsky. HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, стр. 233--234. New York, NY, USA, ACM, (2008)
J. Kim, R. Farzan, и P. Brusilovsky. BooksOnline '08: Proceeding of the 2008 ACM workshop on Research advances in large digital book repositories, стр. 25--28. New York, NY, USA, ACM, (2008)
P. Brusilovsky, H. Hsiao, и M. Yudelson. JCDL '08: Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, стр. 337--340. New York, NY, USA, ACM, (2008)
G. Buscher, A. Dengel, и L. van Elst. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, стр. 387--394. New York, NY, USA, ACM, (2008)
A. Zhang, M. Igo, M. Facciotti, и D. Karger. Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, стр. 319--322. New York, NY, USA, ACM, (2017)
S. Zyto, D. Karger, M. Ackerman, и S. Mahajan. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, стр. 1883--1892. New York, NY, USA, ACM, (2012)
P. Pantel, M. Gamon, O. Alonso, и K. Haas. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, стр. 285--294. New York, NY, USA, ACM, (2012)
A. Zhang, M. Igo, M. Facciotti, и D. Karger. Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, стр. 319--322. New York, NY, USA, ACM, (2017)
S. Bao, G. Xue, X. Wu, Y. Yu, B. Fei, и Z. Su. Proceedings of the 16th International Conference on World Wide Web, стр. 501--510. New York, NY, USA, ACM, (2007)
G. Buscher, A. Dengel, и L. van Elst. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, стр. 387--394. New York, NY, USA, ACM, (2008)
S. Buechel, и U. Hahn. глава Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation, стр. 1--12. Association for Computational Linguistics, (2017)
T. Tran, N. Tran, A. Teka Hadgu, и R. Jäschke. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (сентября 2015)
T. Tran, N. Tran, A. Teka Hadgu, и R. Jäschke. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (сентября 2015)
T. Tran, N. Tran, A. Hadgu, и R. Jäschke. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), стр. 97--106. Association for Computational Linguistics, (сентября 2015)
J. Jeon, V. Lavrenko, и R. Manmatha. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, стр. 119--126. New York, NY, USA, ACM, (2003)