Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Sarridis, Ioannis"'
Computer vision (CV) datasets often exhibit biases that are perpetuated by deep learning models. While recent efforts aim to mitigate these biases and foster fair representations, they fail in complex real-world scenarios. In particular, existing met
Externí odkaz:
http://arxiv.org/abs/2408.11439
AI systems rely on extensive training on large datasets to address various tasks. However, image-based systems, particularly those used for demographic attribute prediction, face significant challenges. Many current face image datasets primarily focu
Externí odkaz:
http://arxiv.org/abs/2404.17255
Autor:
Melzi, Pietro, Tolosana, Ruben, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, DeAndres-Tame, Ivan, Morales, Aythami, Fierrez, Julian, Ortega-Garcia, Javier, Zhao, Weisong, Zhu, Xiangyu, Yan, Zheyu, Zhang, Xiao-Yu, Wu, Jinlin, Lei, Zhen, Tripathi, Suvidha, Kothari, Mahak, Zama, Md Haider, Deb, Debayan, Biesseck, Bernardo, Vidal, Pedro, Granada, Roger, Fickel, Guilherme, Führ, Gustavo, Menotti, David, Unnervik, Alexander, George, Anjith, Ecabert, Christophe, Shahreza, Hatef Otroshi, Rahimi, Parsa, Marcel, Sébastien, Sarridis, Ioannis, Koutlis, Christos, Baltsou, Georgia, Papadopoulos, Symeon, Diou, Christos, Di Domenico, Nicolò, Borghi, Guido, Pellegrini, Lorenzo, Mas-Candela, Enrique, Sánchez-Pérez, Ángela, Atzori, Andrea, Boutros, Fadi, Damer, Naser, Fenu, Gianni, Marras, Mirko
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face R
Externí odkaz:
http://arxiv.org/abs/2311.10476
Deep learning-based person identification and verification systems have remarkably improved in terms of accuracy in recent years; however, such systems, including widely popular cloud-based solutions, have been found to exhibit significant biases rel
Externí odkaz:
http://arxiv.org/abs/2307.10011
Exposure to disturbing imagery can significantly impact individuals, especially professionals who encounter such content as part of their work. This paper presents a user study, involving 107 participants, predominantly journalists and human rights i
Externí odkaz:
http://arxiv.org/abs/2307.10334
Bias in computer vision systems can perpetuate or even amplify discrimination against certain populations. Considering that bias is often introduced by biased visual datasets, many recent research efforts focus on training fair models using such data
Externí odkaz:
http://arxiv.org/abs/2304.14252
The sheer volume of online user-generated content has rendered content moderation technologies essential in order to protect digital platform audiences from content that may cause anxiety, worry, or concern. Despite the efforts towards developing aut
Externí odkaz:
http://arxiv.org/abs/2212.00668
Autor:
Sarridis, Ioannis, Koutlis, Christos, Kordopatis-Zilos, Giorgos, Kompatsiaris, Ioannis, Papadopoulos, Symeon
In this paper we introduce InDistill, a model compression approach that combines knowledge distillation and channel pruning in a unified framework for the transfer of the critical information flow paths from a heavyweight teacher to a lightweight stu
Externí odkaz:
http://arxiv.org/abs/2205.10003
Autor:
Melzi, Pietro, Tolosana, Ruben, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, DeAndres-Tame, Ivan, Morales, Aythami, Fierrez, Julian, Ortega-Garcia, Javier, Zhao, Weisong, Zhu, Xiangyu, Yan, Zheyu, Zhang, Xiao-Yu, Wu, Jinlin, Lei, Zhen, Tripathi, Suvidha, Kothari, Mahak, Zama, Md Haider, Deb, Debayan, Biesseck, Bernardo, Vidal, Pedro, Granada, Roger, Fickel, Guilherme, Führ, Gustavo, Menotti, David, Unnervik, Alexander, George, Anjith, Ecabert, Christophe, Shahreza, Hatef Otroshi, Rahimi, Parsa, Marcel, Sébastien, Sarridis, Ioannis, Koutlis, Christos, Baltsou, Georgia, Papadopoulos, Symeon, Diou, Christos, Domenico, Nicolò Di, Borghi, Guido, Pellegrini, Lorenzo, Mas-Candela, Enrique, Sánchez-Pérez, Ángela, Atzori, Andrea, Boutros, Fadi, Damer, Naser, Fenu, Gianni, Marras, Mirko
Publikováno v:
In Information Fusion July 2024 107
The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizati
Externí odkaz:
http://arxiv.org/abs/1908.08281