Zobrazeno 1 - 10
of 1 220
pro vyhledávání: '"Vasilakos, P."'
Autor:
Li, Jialiang, Wang, Haoyue, Li, Sheng, Qian, Zhenxing, Zhang, Xinpeng, Vasilakos, Athanasios V.
Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fake images. To attribute the AI-generated images, existing schemes usua
Externí odkaz:
http://arxiv.org/abs/2407.14570
ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction
Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks. Protein domains are "building blo
Externí odkaz:
http://arxiv.org/abs/2405.15158
In this paper, we address the challenging source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation, given only a pinhole image pre-trained model (i.e., source) and unlabeled panoramic images (i.e., target). Ta
Externí odkaz:
http://arxiv.org/abs/2404.16501
Unsupervised visible-infrared person re-identification (UVI-ReID) has recently gained great attention due to its potential for enhancing human detection in diverse environments without labeling. Previous methods utilize intra-modality clustering and
Externí odkaz:
http://arxiv.org/abs/2404.06683
Autor:
Jiang, Tianqi, Luo, Haoxiang, Yang, Kun, Sun, Gang, Yu, Hongfang, Huang, Qi, Vasilakos, Athanasios V.
The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market eq
Externí odkaz:
http://arxiv.org/abs/2403.20045
This paper addresses an interesting yet challenging problem -- source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation -- given only a pinhole image-trained model (i.e., source) and unlabeled panoramic images
Externí odkaz:
http://arxiv.org/abs/2403.12505
Autor:
Nguyen, Tri, Nguyen, Huong, Ijaz, Ahmad, Sheikhi, Saeid, Vasilakos, Athanasios V., Kostakos, Panos
The rapid integration of Generative AI (GenAI) and Large Language Models (LLMs) in sectors such as education and healthcare have marked a significant advancement in technology. However, this growth has also led to a largely unexplored aspect: their s
Externí odkaz:
http://arxiv.org/abs/2403.12239
Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a non-trivial task. Some endeavors have been recently made to enhance low-light images using convolutional neural net
Externí odkaz:
http://arxiv.org/abs/2312.13265
Autor:
Vasilakos, Xenofon, Moazzeni, Shadi, Bravalheri, Anderson, Jaisudthi, Pratchaya, Nejabati, Reza, Simeonidou, Dimitra
Leveraging the potential of Virtualised Network Functions (VNFs) requires a clear understanding of the link between resource consumption and performance. The current state of the art tries to do that by utilising Machine Learning (ML) and specificall
Externí odkaz:
http://arxiv.org/abs/2312.09355
Autor:
Li, Haiyuan, Liu, Yuelin, Zhou, Xueqing, Vasilakos, Xenofon, Nejabati, Reza, Yan, Shuangyi, Simeonidou, Dimitra
Multi-access edge computing provides local resources in mobile networks as the essential means for meeting the demands of emerging ultra-reliable low-latency communications. At the edge, dynamic computing requests require advanced resource management
Externí odkaz:
http://arxiv.org/abs/2310.17523