Zobrazeno 1 - 10
of 1 697
pro vyhledávání: '"Muhammad, Haris"'
Autor:
He, Xilin, Luo, Cheng, Xian, Xiaole, Li, Bing, Song, Siyang, Khan, Muhammad Haris, Xie, Weicheng, Shen, Linlin, Ge, Zongyuan
Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection. This limitation poses a significant challenge for developing modern deep learning-based fa
Externí odkaz:
http://arxiv.org/abs/2410.09865
The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue. In this paper, we introduce an innovative integration of multi-modal dat
Externí odkaz:
http://arxiv.org/abs/2409.18715
Autor:
Lykov, Artem, Cabrera, Miguel Altamirano, Konenkov, Mikhail, Serpiva, Valerii, Gbagbe, Koffivi Fid`ele, Alabbas, Ali, Fedoseev, Aleksey, Moreno, Luis, Khan, Muhammad Haris, Guo, Ziang, Tsetserukou, Dzmitry
This paper presents the concept of Industry 6.0, introducing the world's first fully automated production system that autonomously handles the entire product design and manufacturing process based on user-provided natural language descriptions. By le
Externí odkaz:
http://arxiv.org/abs/2409.10106
Despite promising progress in face swapping task, realistic swapped images remain elusive, often marred by artifacts, particularly in scenarios involving high pose variation, color differences, and occlusion. To address these issues, we propose a nov
Externí odkaz:
http://arxiv.org/abs/2409.07269
Autor:
Galappaththige, Chamuditha Jayanaga, Izzo, Zachary, He, Xilin, Zhou, Honglu, Khan, Muhammad Haris
Unarguably, deep learning models capable of generalizing to unseen domain data while leveraging a few labels are of great practical significance due to low developmental costs. In search of this endeavor, we study the challenging problem of semi-supe
Externí odkaz:
http://arxiv.org/abs/2409.03509
Vision-language models (VLMs), e.g., CLIP, have shown remarkable potential in zero-shot image classification. However, adapting these models to new domains remains challenging, especially in unsupervised settings where labelled data is unavailable. R
Externí odkaz:
http://arxiv.org/abs/2408.08855
Autor:
Saeed, Muhammad Saad, Nawaz, Shah, Zaheer, Muhammad Zaigham, Khan, Muhammad Haris, Nandakumar, Karthik, Yousaf, Muhammad Haroon, Sajjad, Hassan, De Schepper, Tom, Schedl, Markus
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that, due to the reliance on fusion strategies, exhibit deteriorated perfo
Externí odkaz:
http://arxiv.org/abs/2408.07445
The exploration of video content via Self-Supervised Learning (SSL) models has unveiled a dynamic field of study, emphasizing both the complex challenges and unique opportunities inherent in this area. Despite the growing body of research, the abilit
Externí odkaz:
http://arxiv.org/abs/2408.00498
The global transportation industry has become one of the main contributors to air pollution. Consequently, electric buses and green transportation are gaining popularity as crucial steps to reduce emission concerns. Many developed countries have alre
Externí odkaz:
http://arxiv.org/abs/2407.20139
Autor:
Liaqat, Muhammad Irzam, Nawaz, Shah, Zaheer, Muhammad Zaigham, Saeed, Muhammad Saad, Sajjad, Hassan, De Schepper, Tom, Nandakumar, Karthik, Schedl, Muhammad Haris Khan Markus
Multimodal learning has demonstrated remarkable performance improvements over unimodal architectures. However, multimodal learning methods often exhibit deteriorated performances if one or more modalities are missing. This may be attributed to the co
Externí odkaz:
http://arxiv.org/abs/2407.16243