2022 |
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Low S., I. N., Nina O., Sappa A. and Blasch E. (2022). Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. In Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 417–425).
Abstract: This paper details the results and main findings of the
second iteration of the Multi-modal Aerial View Object
Classification (MAVOC) challenge. This year’s MAVOC
challenge is the second iteration. The primary goal of
both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input
modalities. Teams are encouraged/challenged to develop
multi-modal approaches that incorporate complementary
information from both domains. While the 2021 challenge
showed a proof of concept that both modalities could be
used together, the 2022 challenge focuses on the detailed
multi-modal models. Using the same UNIfied COincident
Optical and Radar for recognitioN (UNICORN) dataset and
competition format that was used in 2021. Specifically, the
challenge focuses on two techniques, (1) SAR classification
and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods
and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For
SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement
from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average
improvement over 2021.
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Nayeth I. Solorzano, L. C. H., Leslie del R. Lima, Dennys F. Paillacho & Jonathan S. Paillacho. (2022). Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework. In Smart Innovation, Systems and Technologies. International Conference in Information Technology & Education (ICITED 21), julio 15-17 (Vol. 256, pp. 119–132).
Abstract: In gamification, the development of "visual metrics for educational
video games linked to social assistance robots in the framework of inclusive education" seeks to provide support, not only to regular children but also to children with specific psychosocial disabilities, such as those diagnosed with autism spectrum disorder (ASD). However, personalizing each child's experiences represents a limitation, especially for those with atypical behaviors. 'LOLY,' a social assistance robot, works together with mobile applications associated with the family of educational video game series called 'MIDI-AM,' forming a social robotic platform. This platform offers the user curricular digital content to reinforce the teaching-learning processes and motivate regular children and those with ASD. In the present study, technical, programmatic experiments and focus groups were carried out, using open-source facial recognition algorithms to monitor and evaluate the degree of user attention throughout the interaction. The objective is to evaluate the management of a social robot linked to educational video games
through established metrics, which allow monitoring the user's facial expressions
during its use and define a scenario that ensures consistency in the results for its applicability in therapies and reinforcement in the teaching process, mainly
adaptable for inclusive early childhood education.
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Patricia L. Suarez, D. C., Angel D. Sappa and Henry O. Velesaca. (2022). Transformer based Image Dehazing. In 16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. (pp. 148–154).
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Patricia Suarez, H. V., Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos. (2022). Deep Learning based Shrimp Classification. In 17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) (Vol. 13598 LNCS, pp. 36–45).
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