Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Mathur, Puneet"'
Autor:
Luera, Reuben, Rossi, Ryan A., Siu, Alexa, Dernoncourt, Franck, Yu, Tong, Kim, Sungchul, Zhang, Ruiyi, Chen, Xiang, Salehy, Hanieh, Zhao, Jian, Basu, Samyadeep, Mathur, Puneet, Lipka, Nedim
The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so. Current human-AI interaction literature has taken a broad look at how humans interact with generative AI, but it lacks specifi
Externí odkaz:
http://arxiv.org/abs/2410.22370
Autor:
Van Nguyen, Chien, Nguyen, Huy Huu, Pham, Thang M., Zhang, Ruiyi, Deilamsalehy, Hanieh, Mathur, Puneet, Rossi, Ryan A., Bui, Trung, Lai, Viet Dac, Dernoncourt, Franck, Nguyen, Thien Huu
Efficient long-context language modeling remains a significant challenge in Natural Language Processing (NLP). While Transformers dominate language tasks, they struggle with long sequences due to quadratic computational complexity in training and lin
Externí odkaz:
http://arxiv.org/abs/2410.18572
Autor:
Suri, Manan, Mathur, Puneet, Dernoncourt, Franck, Jain, Rajiv, Morariu, Vlad I, Sawhney, Ramit, Nakov, Preslav, Manocha, Dinesh
Document structure editing involves manipulating localized textual, visual, and layout components in document images based on the user's requests. Past works have shown that multimodal grounding of user requests in the document image and identifying
Externí odkaz:
http://arxiv.org/abs/2410.16472
Autor:
Biswas, Sanket, Jain, Rajiv, Morariu, Vlad I., Gu, Jiuxiang, Mathur, Puneet, Wigington, Curtis, Sun, Tong, Lladós, Josep
While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge. This paper delves into this advanced domain, proposing a novel approach
Externí odkaz:
http://arxiv.org/abs/2406.08354
Autor:
Mittal, Trisha, Mathur, Puneet, Chandra, Rohan, Bhatt, Apurva, Gupta, Vikram, Mukherjee, Debdoot, Bera, Aniket, Manocha, Dinesh
We present a computational approach for estimating emotion contagion on social media networks. Built on a foundation of psychology literature, our approach estimates the degree to which the perceivers' emotional states (positive or negative) start to
Externí odkaz:
http://arxiv.org/abs/2207.07165
Autor:
Gupta, Vikram, Mittal, Trisha, Mathur, Puneet, Mishra, Vaibhav, Maheshwari, Mayank, Bera, Aniket, Mukherjee, Debdoot, Manocha, Dinesh
We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K u
Externí odkaz:
http://arxiv.org/abs/2203.14456
We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas
Externí odkaz:
http://arxiv.org/abs/2103.06541
We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram~(EEG) and Eye movement~(EM) data in order to help differentiate between normal reading and task-oriented reading. Understanding the physiol
Externí odkaz:
http://arxiv.org/abs/2102.11922