Zobrazeno 1 - 10
of 2 201
pro vyhledávání: '"Raviteja, A."'
Autor:
Chatterjee, Soumick, Mattern, Hendrik, Dörner, Marc, Sciarra, Alessandro, Dubost, Florian, Schnurre, Hannes, Khatun, Rupali, Yu, Chun-Chih, Hsieh, Tsung-Lin, Tsai, Yi-Shan, Fang, Yi-Zeng, Yang, Yung-Ching, Huang, Juinn-Dar, Xu, Marshall, Liu, Siyu, Ribeiro, Fernanda L., Bollmann, Saskia, Chintalapati, Karthikesh Varma, Radhakrishna, Chethan Mysuru, Kumara, Sri Chandana Hudukula Ram, Sutrave, Raviteja, Qayyum, Abdul, Mazher, Moona, Razzak, Imran, Rodero, Cristobal, Niederren, Steven, Lin, Fengming, Xia, Yan, Wang, Jiacheng, Qiu, Riyu, Wang, Liansheng, Panah, Arya Yazdan, Jurdi, Rosana El, Fu, Guanghui, Arslan, Janan, Vaillant, Ghislain, Valabregue, Romain, Dormont, Didier, Stankoff, Bruno, Colliot, Olivier, Vargas, Luisa, Chacón, Isai Daniel, Pitsiorlas, Ioannis, Arbeláez, Pablo, Zuluaga, Maria A., Schreiber, Stefanie, Speck, Oliver, Nürnberger, Andreas
The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe condi
Externí odkaz:
http://arxiv.org/abs/2411.09593
Autor:
Chappa, Naga VS Raviteja, McCormick, Charlotte, Gongora, Susana Rodriguez, Dobbs, Page Daniel, Luu, Khoa
The Public Health Advocacy Dataset (PHAD) is a comprehensive collection of 5,730 videos related to tobacco products sourced from social media platforms like TikTok and YouTube. This dataset encompasses 4.3 million frames and includes detailed metadat
Externí odkaz:
http://arxiv.org/abs/2411.13572
Group Activity Recognition (GAR) remains challenging in computer vision due to the complex nature of multi-agent interactions. This paper introduces LiGAR, a LIDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition. LiGAR leve
Externí odkaz:
http://arxiv.org/abs/2410.21108
The proliferation of tobacco-related content on social media platforms poses significant challenges for public health monitoring and intervention. This paper introduces a novel multi-modal deep learning framework named Flow-Attention Adaptive Semanti
Externí odkaz:
http://arxiv.org/abs/2410.19896
Autor:
Mou, Shancong, Vemulapalli, Raviteja, Li, Shiyu, Liu, Yuxuan, Thomas, C, Cao, Meng, Bai, Haoping, Tuzel, Oncel, Huang, Ping, Shan, Jiulong, Shi, Jianjun
Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect data generation is a popular approach for mitigating data challenges. How
Externí odkaz:
http://arxiv.org/abs/2410.18490
While server-side Large Language Models (LLMs) demonstrate proficiency in function calling and complex reasoning, deploying Small Language Models (SLMs) directly on devices brings opportunities to improve latency and privacy but also introduces uniqu
Externí odkaz:
http://arxiv.org/abs/2410.09407
Autor:
Pusateri, Ernest, Walia, Anmol, Kashi, Anirudh, Bandyopadhyay, Bortik, Hyder, Nadia, Mahinder, Sayantan, Anantha, Raviteja, Liu, Daben, Gondala, Sashank
In recent years, end-to-end automatic speech recognition (ASR) systems have proven themselves remarkably accurate and performant, but these systems still have a significant error rate for entity names which appear infrequently in their training data.
Externí odkaz:
http://arxiv.org/abs/2409.06062
Generating user intent from a sequence of user interface (UI) actions is a core challenge in comprehensive UI understanding. Recent advancements in multimodal large language models (MLLMs) have led to substantial progress in this area, but their dema
Externí odkaz:
http://arxiv.org/abs/2409.04081
Autor:
Echterhoff, Jessica, Faghri, Fartash, Vemulapalli, Raviteja, Hu, Ting-Yao, Li, Chun-Liang, Tuzel, Oncel, Pouransari, Hadi
Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. Within the update process, developers often prioritize improving overall performance metrics, paying less attention to maint
Externí odkaz:
http://arxiv.org/abs/2407.09435
Autor:
Radhakrishna, Chethan, Chintalapati, Karthikesh Varma, Kumar, Sri Chandana Hudukula Ram, Sutrave, Raviteja, Mattern, Hendrik, Speck, Oliver, Nürnberger, Andreas, Chatterjee, Soumick
Identification of vessel structures of different sizes in biomedical images is crucial in the diagnosis of many neurodegenerative diseases. However, the sparsity of good-quality annotations of such images makes the task of vessel segmentation challen
Externí odkaz:
http://arxiv.org/abs/2407.08655