Zobrazeno 1 - 10
of 9 949
pro vyhledávání: '"Boutros, A."'
Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model. In this paper, we present an adaptive KD approach, namely AdaDistill, for deep face recognition
Externí odkaz:
http://arxiv.org/abs/2407.01332
Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-t
Externí odkaz:
http://arxiv.org/abs/2404.12203
Autor:
DeAndres-Tame, Ivan, Tolosana, Ruben, Melzi, Pietro, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, Morales, Aythami, Fierrez, Julian, Ortega-Garcia, Javier, Zhong, Zhizhou, Huang, Yuge, Mi, Yuxi, Ding, Shouhong, Zhou, Shuigeng, He, Shuai, Fu, Lingzhi, Cong, Heng, Zhang, Rongyu, Xiao, Zhihong, Smirnov, Evgeny, Pimenov, Anton, Grigorev, Aleksei, Timoshenko, Denis, Asfaw, Kaleb Mesfin, Low, Cheng Yaw, Liu, Hao, Wang, Chuyi, Zuo, Qing, He, Zhixiang, Shahreza, Hatef Otroshi, George, Anjith, Unnervik, Alexander, Rahimi, Parsa, Marcel, Sébastien, Neto, Pedro C., Huber, Marco, Kolf, Jan Niklas, Damer, Naser, Boutros, Fadi, Cardoso, Jaime S., Sequeira, Ana F., Atzori, Andrea, Fenu, Gianni, Marras, Mirko, Štruc, Vitomir, Yu, Jiang, Li, Zhangjie, Li, Jichun, Zhao, Weisong, Lei, Zhen, Zhu, Xiangyu, Zhang, Xiao-Yu, Biesseck, Bernardo, Vidal, Pedro, Coelho, Luiz, Granada, Roger, Menotti, David
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRw 2024)
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some c
Externí odkaz:
http://arxiv.org/abs/2404.10378
Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQ
Externí odkaz:
http://arxiv.org/abs/2404.09555
Deep learning (DL) is becoming the cornerstone of numerous applications both in datacenters and at the edge. Specialized hardware is often necessary to meet the performance requirements of state-of-the-art DL models, but the rapid pace of change in D
Externí odkaz:
http://arxiv.org/abs/2404.10076
Autor:
Shahreza, Hatef Otroshi, Ecabert, Christophe, George, Anjith, Unnervik, Alexander, Marcel, Sébastien, Di Domenico, Nicolò, Borghi, Guido, Maltoni, Davide, Boutros, Fadi, Vogel, Julia, Damer, Naser, Sánchez-Pérez, Ángela, EnriqueMas-Candela, Calvo-Zaragoza, Jorge, Biesseck, Bernardo, Vidal, Pedro, Granada, Roger, Menotti, David, DeAndres-Tame, Ivan, La Cava, Simone Maurizio, Concas, Sara, Melzi, Pietro, Tolosana, Ruben, Vera-Rodriguez, Ruben, Perelli, Gianpaolo, Orrù, Giulia, Marcialis, Gian Luca, Fierrez, Julian
Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating syn
Externí odkaz:
http://arxiv.org/abs/2404.04580
Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concer
Externí odkaz:
http://arxiv.org/abs/2404.03537
Autor:
Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Bletz, Julie, Albrecht, Jacob, Andreoletti, Gaia, Costello, James C., Boutros, Paul
Data Science research is undergoing a revolution fueled by the transformative power of technology, the Internet, and an ever increasing computational capacity. The rate at which sophisticated algorithms can be developed is unprecedented, yet they rem
Externí odkaz:
http://arxiv.org/abs/2312.05296
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
Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensiti
Externí odkaz:
http://arxiv.org/abs/2311.03970