Zobrazeno 1 - 10
of 1 011 964
pro vyhledávání: '"A, Khan"'
Autor:
Laskar, Md Tahmid Rahman, Alqahtani, Sawsan, Bari, M Saiful, Rahman, Mizanur, Khan, Mohammad Abdullah Matin, Khan, Haidar, Jahan, Israt, Bhuiyan, Amran, Tan, Chee Wei, Parvez, Md Rizwan, Hoque, Enamul, Joty, Shafiq, Huang, Jimmy
Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-w
Externí odkaz:
http://arxiv.org/abs/2407.04069
Autor:
Gupta, Akshita, Arora, Aditya, Narayan, Sanath, Khan, Salman, Khan, Fahad Shahbaz, Taylor, Graham W.
Open-Vocabulary Temporal Action Localization (OVTAL) enables a model to recognize any desired action category in videos without the need to explicitly curate training data for all categories. However, this flexibility poses significant challenges, as
Externí odkaz:
http://arxiv.org/abs/2406.15556
The recent developments in Large Multi-modal Video Models (Video-LMMs) have significantly enhanced our ability to interpret and analyze video data. Despite their impressive capabilities, current Video-LMMs have not been evaluated for anomaly detectio
Externí odkaz:
http://arxiv.org/abs/2406.10326
Autor:
Agrawal, A., Alenkov, V. V., Aryal, P., Beyer, J., Bhandari, B., Boiko, R. S., Boonin, K., Buzanov, O., Byeon, C. R., Chanthima, N., Cheoun, M. K., Choe, J. S., Choi, Seonho, Choudhury, S., Chung, J. S., Danevich, F. A., Djamal, M., Drung, D., Enss, C., Fleischmann, A., Gangapshev, A. M., Gastaldo, L., Gavrilyuk, Y. M., Gezhaev, A. M., Gileva, O., Grigorieva, V. D., Gurentsov, V. I., Ha, C., Ha, D. H., Ha, E. J., Hwnag, D. H., Jeona, E. J., Jeon, J. A., Jo, H. S., Kaewkhao, J., Kang, C. S., Kang, W. G., Kazalov, V. V., Kempf, S., Khan, A., Khan, S., Kim, D. Y., Kim, G. W., Kim, H. B., Kim, Ho-Jong, Kim, H. J., Kim, H. L., Kim, H. S., Kim, M. B., Kim, S. C., Kim, S. K., Kim, S. R., Kim, W. T., Kim, Y. D., Kim, Y. H., Kirdsiri, K., Ko, Y. J., Kobychev, V. V., Kuzminov, V. Kornoukhov V. V., Kwon, D. H., Lee, C. H., Lee, DongYeup, Lee, E. K., Lee, H. J., Lee, H. S., Lee, J., Lee, J. Y., Lee, K. B., Lee, M. H., Lee, M. K., Lee, S. W., Lee, Y. C., Leonard, D. S., Lim, H. S., Mailyan, B., Makarov, E. P., Nyanda, P., Oh, Y., Olsen, S. L., Panasenko, S. I., Park, H. K., Park, H. S., Park, K. S., Park, S. Y., Polischuk, O. G., Prihtiadi, H., Ra, S., Rooh, S. S. Ratkevich G., Sari, M. B., Seob, J., Seo, K. M., Sharma, B., Shin, K. A., Shlegel, V. N., Siyeon, K., So, J., Sokur, N. V., Son, J. K., Song, J. W., Srisittipokakun, N., Tretyak, V. I., Wirawan, R., Woo, K. R., Yeon, H. J., Yoon, Y. S., Yue, Q.
AMoRE-II aims to search for neutrinoless double beta decay with an array of 423 Li$_2$$^{100}$MoO$_4$ crystals operating in the cryogenic system as the main phase of the Advanced Molybdenum-based Rare process Experiment (AMoRE). AMoRE has been planne
Externí odkaz:
http://arxiv.org/abs/2406.09698
Building on the advances of language models, Large Multimodal Models (LMMs) have contributed significant improvements in video understanding. While the current video LMMs utilize advanced Large Language Models (LLMs), they rely on either image or vid
Externí odkaz:
http://arxiv.org/abs/2406.09418
Autor:
Malik, Hashmat Shadab, Shamshad, Fahad, Naseer, Muzammal, Nandakumar, Karthik, Khan, Fahad Shahbaz, Khan, Salman
Vision State Space Models (VSSMs), a novel architecture that combines the strengths of recurrent neural networks and latent variable models, have demonstrated remarkable performance in visual perception tasks by efficiently capturing long-range depen
Externí odkaz:
http://arxiv.org/abs/2406.09407
Autor:
Malik, Hashmat Shadab, Saeed, Numan, Hanif, Asif, Naseer, Muzammal, Yaqub, Mohammad, Khan, Salman, Khan, Fahad Shahbaz
Volumetric medical segmentation models have achieved significant success on organ and tumor-based segmentation tasks in recent years. However, their vulnerability to adversarial attacks remains largely unexplored, raising serious concerns regarding t
Externí odkaz:
http://arxiv.org/abs/2406.08486
In Multi-agent Reinforcement Learning (MARL), accurately perceiving opponents' strategies is essential for both cooperative and adversarial contexts, particularly within dynamic environments. While Proximal Policy Optimization (PPO) and related algor
Externí odkaz:
http://arxiv.org/abs/2406.06500
Autor:
Usman, Muhammad, Shahid, M Husnain, Ejaz, Maheen, Hani, Ummay, Fatima, Nayab, Khan, Abdul Rehman, Khan, Asifullah, Mirza, Nasir Majid
Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we propose an idea of a new
Externí odkaz:
http://arxiv.org/abs/2406.06638
Autor:
Boudjoghra, Mohamed El Amine, Dai, Angela, Lahoud, Jean, Cholakkal, Hisham, Anwer, Rao Muhammad, Khan, Salman, Khan, Fahad Shahbaz
Recent works on open-vocabulary 3D instance segmentation show strong promise, but at the cost of slow inference speed and high computation requirements. This high computation cost is typically due to their heavy reliance on 3D clip features, which re
Externí odkaz:
http://arxiv.org/abs/2406.02548