Zobrazeno 1 - 10
of 2 312
pro vyhledávání: '"GUPTA, ROHIT"'
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:
Cui, Can, Ma, Yunsheng, Yang, Zichong, Zhou, Yupeng, Liu, Peiran, Lu, Juanwu, Li, Lingxi, Chen, Yaobin, Panchal, Jitesh H., Abdelraouf, Amr, Gupta, Rohit, Han, Kyungtae, Wang, Ziran
With the broader usage and highly successful development of Large Language Models (LLMs), there has been a growth of interest and demand for applying LLMs to autonomous driving technology. Driven by their natural language understanding and reasoning
Externí odkaz:
http://arxiv.org/abs/2410.15281
Multimodal large language models (MLLMs) have demonstrated remarkable potential for enhancing scene understanding in autonomous driving systems through powerful logical reasoning capabilities. However, the deployment of these models faces significant
Externí odkaz:
http://arxiv.org/abs/2409.11182
Autor:
Gupta, Rohit, Rizve, Mamshad Nayeem, Unnikrishnan, Jayakrishnan, Tawari, Ashish, Tran, Son, Shah, Mubarak, Yao, Benjamin, Chilimbi, Trishul
Pre-trained vision-language models (VLMs) have enabled significant progress in open vocabulary computer vision tasks such as image classification, object detection and image segmentation. Some recent works have focused on extending VLMs to open vocab
Externí odkaz:
http://arxiv.org/abs/2407.09073
Autor:
Qin, Ziye, Li, Siyan, Wu, Guoyuan, Barth, Matthew J., Abdelraouf, Amr, Gupta, Rohit, Han, Kyungtae
Dilemma zones at signalized intersections present a commonly occurring but unsolved challenge for both drivers and traffic operators. Onsets of the yellow lights prompt varied responses from different drivers: some may brake abruptly, compromising th
Externí odkaz:
http://arxiv.org/abs/2405.03873
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However, it presents unique challenges, due to the complex roadway layout at intersections, involvement of
Externí odkaz:
http://arxiv.org/abs/2404.11181
Publikováno v:
Benchmarking: An International Journal, 2023, Vol. 31, Issue 9, pp. 2871-2896.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/BIJ-01-2023-0032
Autor:
Ma, Yunsheng, Cui, Can, Cao, Xu, Ye, Wenqian, Liu, Peiran, Lu, Juanwu, Abdelraouf, Amr, Gupta, Rohit, Han, Kyungtae, Bera, Aniket, Rehg, James M., Wang, Ziran
Autonomous driving (AD) has made significant strides in recent years. However, existing frameworks struggle to interpret and execute spontaneous user instructions, such as "overtake the car ahead." Large Language Models (LLMs) have demonstrated impre
Externí odkaz:
http://arxiv.org/abs/2312.04372
Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts. In the context of human-assisted or autonomous driving, explainability models can he
Externí odkaz:
http://arxiv.org/abs/2310.16639
Publikováno v:
Phys. Rev. D 109 (2024), 074032
We compare the relative importance of different mechanisms for polarized $J/\psi$ production in semi-inclusive deep inelastic scattering processes at large $Q^2$. The transverse momentum dependent (TMD) factorization framework and nonrelativistic qua
Externí odkaz:
http://arxiv.org/abs/2310.13737