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pro vyhledávání: '"Khan, Muhammad Haris"'
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
Compositional Zero-Shot Learning (CZSL) aims to predict unknown compositions made up of attribute and object pairs. Predicting compositions unseen during training is a challenging task. We are exploring Open World Compositional Zero-Shot Learning (OW
Externí odkaz:
http://arxiv.org/abs/2407.13715
The purpose of segmentation refinement is to enhance the initial coarse masks generated by segmentation algorithms. The refined masks are expected to capture the details and contours of the target objects. Research on segmentation refinement has deve
Externí odkaz:
http://arxiv.org/abs/2407.04519
Autor:
Danish, Muhammad Sohail, Khan, Muhammad Haris, Munir, Muhammad Akhtar, Sarfraz, M. Saquib, Ali, Mohsen
In this work, we tackle the problem of domain generalization for object detection, specifically focusing on the scenario where only a single source domain is available. We propose an effective approach that involves two key steps: diversifying the so
Externí odkaz:
http://arxiv.org/abs/2405.14497