Zobrazeno 1 - 10
of 1 023 602
pro vyhledávání: '"and, Khan"'
Autor:
Alharthi, Fatemah, Apachigawo, Ishmael, Solanki, Dhruvil, Khan, Sazzad, Singh, Himanshi, Khan, Mohammad Moshahid, Pradhan, Prabhakar
Publikováno v:
International Journal of Molecular Sciences, 25(22),12211 (2024)
Understanding alterations in structural disorders in tissue or cells or building blocks, such as DNA or chromatin in the human brain, at the nano to submicron level provides us with efficient biomarkers for Alzheimers detection. Here, we report a dua
Externí odkaz:
http://arxiv.org/abs/2412.14651
Autor:
Soni, Sagar, Dudhane, Akshay, Debary, Hiyam, Fiaz, Mustansar, Munir, Muhammad Akhtar, Danish, Muhammad Sohail, Fraccaro, Paolo, Watson, Campbell D, Klein, Levente J, Khan, Fahad Shahbaz, Khan, Salman
Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and resource management. Existing generic VLMs do not perform well on Remote
Externí odkaz:
http://arxiv.org/abs/2412.15190
Autor:
Rahmun, Mahieyin, Khan, Rafat Hasan, Aurpa, Tanjim Taharat, Khan, Sadia, Nahiyan, Zulker Nayeen, Almas, Mir Sayad Bin, Rajib, Rakibul Hasan, Hassan, Syeda Sakira
The aim of this project is to implement and design arobust synthetic speech classifier for the IEEE Signal ProcessingCup 2022 challenge. Here, we learn a synthetic speech attributionmodel using the speech generated from various text-to-speech(TTS) al
Externí odkaz:
http://arxiv.org/abs/2412.13279
Autor:
Khattak, Muhammad Uzair, Kunhimon, Shahina, Naseer, Muzammal, Khan, Salman, Khan, Fahad Shahbaz
Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks. However, their application in the medical domain remains limited due to the scarcity of openly accessible, large-scale medical image-
Externí odkaz:
http://arxiv.org/abs/2412.10372
Autor:
Mullappilly, Sahal Shaji, Kurpath, Mohammed Irfan, Pieri, Sara, Alseiari, Saeed Yahya, Cholakkal, Shanavas, Aldahmani, Khaled, Khan, Fahad, Anwer, Rao, Khan, Salman, Baldwin, Timothy, Cholakkal, Hisham
This paper introduces BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model (LMM) with a unified architecture that integrates text and visual modalities, enabling advanced image understanding and medical applications. BiMed
Externí odkaz:
http://arxiv.org/abs/2412.07769
A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a
Externí odkaz:
http://arxiv.org/abs/2412.07175
Autor:
Mattu, Sandesh Rao, Khan, Imran Ali, Khammammetti, Venkatesh, Dabak, Beyza, Mohammed, Saif Khan, Narayanan, Krishna, Calderbank, Robert
Much of the engineering behind current wireless systems has focused on designing an efficient and high-throughput downlink to support human-centric communication such as video streaming and internet browsing. This paper looks ahead to design of the u
Externí odkaz:
http://arxiv.org/abs/2412.04295
Autor:
Danish, Muhammad Sohail, Munir, Muhammad Akhtar, Shah, Syed Roshaan Ali, Kuckreja, Kartik, Khan, Fahad Shahbaz, Fraccaro, Paolo, Lacoste, Alexandre, Khan, Salman
While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they fall short in addressing the unique demands of geospatial applications. Generic VLM benchmarks are not designed to handle the complexities of geospatial
Externí odkaz:
http://arxiv.org/abs/2411.19325
Autor:
Vayani, Ashmal, Dissanayake, Dinura, Watawana, Hasindri, Ahsan, Noor, Sasikumar, Nevasini, Thawakar, Omkar, Ademtew, Henok Biadglign, Hmaiti, Yahya, Kumar, Amandeep, Kuckreja, Kartik, Maslych, Mykola, Ghallabi, Wafa Al, Mihaylov, Mihail, Qin, Chao, Shaker, Abdelrahman M, Zhang, Mike, Ihsani, Mahardika Krisna, Esplana, Amiel, Gokani, Monil, Mirkin, Shachar, Singh, Harsh, Srivastava, Ashay, Hamerlik, Endre, Izzati, Fathinah Asma, Maani, Fadillah Adamsyah, Cavada, Sebastian, Chim, Jenny, Gupta, Rohit, Manjunath, Sanjay, Zhumakhanova, Kamila, Rabevohitra, Feno Heriniaina, Amirudin, Azril, Ridzuan, Muhammad, Kareem, Daniya, More, Ketan, Li, Kunyang, Shakya, Pramesh, Saad, Muhammad, Ghasemaghaei, Amirpouya, Djanibekov, Amirbek, Azizov, Dilshod, Jankovic, Branislava, Bhatia, Naman, Cabrera, Alvaro, Obando-Ceron, Johan, Otieno, Olympiah, Farestam, Fabian, Rabbani, Muztoba, Baliah, Sanoojan, Sanjeev, Santosh, Shtanchaev, Abduragim, Fatima, Maheen, Nguyen, Thao, Kareem, Amrin, Aremu, Toluwani, Xavier, Nathan, Bhatkal, Amit, Toyin, Hawau, Chadha, Aman, Cholakkal, Hisham, Anwer, Rao Muhammad, Felsberg, Michael, Laaksonen, Jorma, Solorio, Thamar, Choudhury, Monojit, Laptev, Ivan, Shah, Mubarak, Khan, Salman, Khan, Fahad
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource
Externí odkaz:
http://arxiv.org/abs/2411.16508
Autor:
Munasinghe, Shehan, Gani, Hanan, Zhu, Wenqi, Cao, Jiale, Xing, Eric, Khan, Fahad Shahbaz, Khan, Salman
Fine-grained alignment between videos and text is challenging due to complex spatial and temporal dynamics in videos. Existing video-based Large Multimodal Models (LMMs) handle basic conversations but struggle with precise pixel-level grounding in vi
Externí odkaz:
http://arxiv.org/abs/2411.04923