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pro vyhledávání: '"Chellappa, Rama"'
Autor:
Rivera, Corban, Byrd, Grayson, Paul, William, Feldman, Tyler, Booker, Meghan, Holmes, Emma, Handelman, David, Kemp, Bethany, Badger, Andrew, Schmidt, Aurora, Jatavallabhula, Krishna Murthy, de Melo, Celso M, Seenivasan, Lalithkumar, Unberath, Mathias, Chellappa, Rama
Robotic planning and execution in open-world environments is a complex problem due to the vast state spaces and high variability of task embodiment. Recent advances in perception algorithms, combined with Large Language Models (LLMs) for planning, of
Externí odkaz:
http://arxiv.org/abs/2410.06108
Publikováno v:
Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II. Vol. 13035. SPIE, 2024
In this work, we explore the possibility of using synthetically generated data for video-based gesture recognition with large pre-trained models. We consider whether these models have sufficiently robust and expressive representation spaces to enable
Externí odkaz:
http://arxiv.org/abs/2410.02152
We introduce FusionRF, a novel neural rendering terrain reconstruction method from optically unprocessed satellite imagery. While previous methods depend on external pansharpening methods to fuse low resolution multispectral imagery and high resoluti
Externí odkaz:
http://arxiv.org/abs/2409.15132
Autor:
Nanduri, Anirudh, Chellappa, Rama
Despite the remarkable performance of deep neural networks for face detection and recognition tasks in the visible spectrum, their performance on more challenging non-visible domains is comparatively still lacking. While significant research has been
Externí odkaz:
http://arxiv.org/abs/2409.09832
Predicting and reasoning how a video would make a human feel is crucial for developing socially intelligent systems. Although Multimodal Large Language Models (MLLMs) have shown impressive video understanding capabilities, they tend to focus more on
Externí odkaz:
http://arxiv.org/abs/2409.00304
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