no code implementations • 14 Aug 2023 • Cole Hill, Mauricio Pamplona Segundo, Sudeep Sarkar
Deep learning research has made many biometric recognition solution viable, but it requires vast training data to achieve real-world generalization.
no code implementations • 14 Aug 2023 • Shenyuan Liang, Mauricio Pamplona Segundo, Sathyanarayanan N. Aakur, Sudeep Sarkar, Anuj Srivastava
This, in turn, requires optimization over the permutation group, made challenging by differences in nodes (in terms of numbers, locations) and edges (in terms of shapes, placements, and sizes) across objects.
1 code implementation • 27 Nov 2022 • Azim Ibragimov, Mauricio Pamplona Segundo
We also present a baseline method inspired on the state-of-the-art that implements a customizable Fully Convolutional Network, whose hyperparameters were tuned to achieve optimal pore detection rates.
1 code implementation • 12 Aug 2022 • Adeilson Antonio da Silva, Mauricio Pamplona Segundo
Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification.
1 code implementation • 21 Apr 2021 • Mauricio Pamplona Segundo, Allan Pinto, Rodrigo Minetto, Ricardo da Silva Torres, Sudeep Sarkar
This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas.
2 code implementations • 16 Apr 2020 • Rodrigo Minetto, Mauricio Pamplona Segundo, Gilbert Rotich, Sudeep Sarkar
We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators.
2 code implementations • 5 Feb 2020 • André Brasil Vieira Wyzykowski, Mauricio Pamplona Segundo, Rubisley de Paula Lemes
Given that we also favorably compare our results with the most advanced works in the literature, our experimentation suggests that our approach is the new state-of-the-art.
no code implementations • 11 Mar 2019 • Žiga Emeršič, Aruna Kumar S. V., B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar, Hyeonjung Park, Gi Pyo Nam, Ig-Jae Kim, Sagar G. Sangodkar, Ümit Kaçar, Murvet Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, Hazim Kemal Ekenel, Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi, Peter Peer, Vitomir Štruc
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
1 code implementation • 10 Feb 2018 • Rodrigo Minetto, Mauricio Pamplona Segundo, Sudeep Sarkar
With this framework, we were able to reduce the training time while maintaining the classification performance of the ensemble.
1 code implementation • 20 Oct 2017 • Earnest E. Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar
We used the results generated to perform a geometric image normalization that boosted the performance of all evaluated descriptors.