Zobrazeno 1 - 10
of 1 318
pro vyhledávání: '"Chellappa, Rama"'
Learning models whose predictions are invariant under multiple environments is a promising approach for out-of-distribution generalization. Such models are trained to extract features $X_{\text{inv}}$ where the conditional distribution $Y \mid X_{\te
Externí odkaz:
http://arxiv.org/abs/2407.18428
Seeking answers to questions within long scientific research articles is a crucial area of study that aids readers in quickly addressing their inquiries. However, existing question-answering (QA) datasets based on scientific papers are limited in sca
Externí odkaz:
http://arxiv.org/abs/2407.09413
Autor:
Zhang, Zhaoliang, Song, Tianchen, Lee, Yongjae, Yang, Li, Peng, Cheng, Chellappa, Rama, Fan, Deliang
Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream methodologies for novel view synthesis (NVS) due to its high quality and fast rendering speed. However, as a point-based scene representation, 3DGS potentially generates a large
Externí odkaz:
http://arxiv.org/abs/2405.18784
Autor:
Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yao, Yue, Zheng, Liang, Rahman, Mohammed Shaiqur, Arya, Meenakshi S., Sharma, Anuj, Chakraborty, Pranamesh, Prajapati, Sanjita, Kong, Quan, Kobori, Norimasa, Gochoo, Munkhjargal, Otgonbold, Munkh-Erdene, Alnajjar, Fady, Batnasan, Ganzorig, Chen, Ping-Yang, Hsieh, Jun-Wei, Wu, Xunlei, Pusegaonkar, Sameer Satish, Wang, Yizhou, Biswas, Sujit, Chellappa, Rama
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition f
Externí odkaz:
http://arxiv.org/abs/2404.09432
Autor:
Souri, Hossein, Bansal, Arpit, Kazemi, Hamid, Fowl, Liam, Saha, Aniruddha, Geiping, Jonas, Wilson, Andrew Gordon, Chellappa, Rama, Goldstein, Tom, Goldblum, Micah
Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection. As a result of this insecure curation pipeline, an adversary can poison or backdoor the resulting model by uploading malicious data to th
Externí odkaz:
http://arxiv.org/abs/2403.16365
In this work, we introduce FaceXformer, an end-to-end unified transformer model for a comprehensive range of facial analysis tasks such as face parsing, landmark detection, head pose estimation, attributes recognition, and estimation of age, gender,
Externí odkaz:
http://arxiv.org/abs/2403.12960
Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry. In this work, we analyze the robustness of Gaussian-S
Externí odkaz:
http://arxiv.org/abs/2403.04926
Autor:
Schmidgall, Samuel, Harris, Carl, Essien, Ime, Olshvang, Daniel, Rahman, Tawsifur, Kim, Ji Woong, Ziaei, Rojin, Eshraghian, Jason, Abadir, Peter, Chellappa, Rama
There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While promising, exam questions do not reflect the complexity of real patien
Externí odkaz:
http://arxiv.org/abs/2402.08113
Autor:
Suin, Maitreya, Chellappa, Rama
Recent generative-prior-based methods have shown promising blind face restoration performance. They usually project the degraded images to the latent space and then decode high-quality faces either by single-stage latent optimization or directly from
Externí odkaz:
http://arxiv.org/abs/2402.06106
Autor:
Shah, Ketul, Crandall, Robert, Xu, Jie, Zhou, Peng, George, Marian, Bansal, Mayank, Chellappa, Rama
Videos captured from multiple viewpoints can help in perceiving the 3D structure of the world and benefit computer vision tasks such as action recognition, tracking, etc. In this paper, we present a method for self-supervised learning from synchroniz
Externí odkaz:
http://arxiv.org/abs/2401.15900