Zobrazeno 1 - 10
of 360
pro vyhledávání: '"Escalante, Hugo"'
We address the challenge of detecting questionable content in online media, specifically the subcategory of comic mischief. This type of content combines elements such as violence, adult content, or sarcasm with humor, making it difficult to detect.
Externí odkaz:
http://arxiv.org/abs/2406.07841
Autor:
Yuan, Haocheng, Liu, Ajian, Zheng, Junze, Wan, Jun, Deng, Jiankang, Escalera, Sergio, Escalante, Hugo Jair, Guyon, Isabelle, Lei, Zhen
Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which po
Externí odkaz:
http://arxiv.org/abs/2404.06211
Publikováno v:
Pattern Recognition Letters (2024)
Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants rec
Externí odkaz:
http://arxiv.org/abs/2403.04693
Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of
Externí odkaz:
http://arxiv.org/abs/2312.00268
We introduce TurboGP, a Genetic Programming (GP) library fully written in Python and specifically designed for machine learning tasks. TurboGP implements modern features not available in other GP implementations, such as island and cellular populatio
Externí odkaz:
http://arxiv.org/abs/2309.00149
We propose a novel approach for the challenge of designing less complex yet highly effective convolutional neural networks (CNNs) through the use of cartesian genetic programming (CGP) for neural architecture search (NAS). Our approach combines real-
Externí odkaz:
http://arxiv.org/abs/2306.02648
In recent decades, challenges have become very popular in scientific research as these are crowdsourcing schemes. In particular, challenges are essential for developing machine learning algorithms. For the challenges settings, it is vital to establis
Externí odkaz:
http://arxiv.org/abs/2305.10452
Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone
Externí odkaz:
http://arxiv.org/abs/2304.07580
Autor:
Wang, Dong, Guo, Jia, Shao, Qiqi, He, Haochi, Chen, Zhian, Xiao, Chuanbao, Liu, Ajian, Escalera, Sergio, Escalante, Hugo Jair, Lei, Zhen, Wan, Jun, Deng, Jiankang
Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This
Externí odkaz:
http://arxiv.org/abs/2304.05753