Abstract
The remarkable performance of deep learning models is heavily dependent on the availability of large and diverse amounts of training data and its correlation with the target application scenario. This is especially crucial in robotics, where the deployment environments often differ from the training ones. Domain generalization (DG) techniques investigate this problem by leveraging data from multiple source domains so that a trained model can generalize to unseen domains. In this work, we thoroughly evaluate the performance in the classification of kitchen utensils of several state-of-the-art DG methods. Extensive experiments on the seven domains that compose the Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation) dataset show that the effectiveness of some of the DG methods varies across domains, with none performing well across all of them. Specifically, most methods achieved high accuracy rates in four of the seven datasets, and while there was a reasonable improvement in the most difficult domains, there is still ample room for further research.
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Acknowledgments
This paper is part of the project I+D+i PID2020-118447RA-I00 (MultiScore), funded by MCIN/AEI/10.13039/501100011033. The first author is supported by grant CIACIF/2021/465 from “Programa I+D+i de la Generalitat Valenciana“. The second author is supported by grant FPU19/04957 from the Spanish Ministerio de Universidades.
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Garrido-Munoz, C., Alfaro-Contreras, M., Calvo-Zaragoza, J. (2023). Evaluating Domain Generalization in Kitchen Utensils Classification. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_9
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