Zobrazeno 1 - 10
of 309
pro vyhledávání: '"Patel, Deep"'
Autor:
Chen, Yuxiao, Li, Kai, Bao, Wentao, Patel, Deep, Kong, Yu, Min, Martin Renqiang, Metaxas, Dimitris N.
Learning to localize temporal boundaries of procedure steps in instructional videos is challenging due to the limited availability of annotated large-scale training videos. Recent works focus on learning the cross-modal alignment between video segmen
Externí odkaz:
http://arxiv.org/abs/2409.16145
Multi-camera tracking plays a pivotal role in various real-world applications. While end-to-end methods have gained significant interest in single-camera tracking, multi-camera tracking remains predominantly reliant on heuristic techniques. In respon
Externí odkaz:
http://arxiv.org/abs/2408.13243
JPEG remains one of the most widespread lossy image coding methods. However, the non-differentiable nature of JPEG restricts the application in deep learning pipelines. Several differentiable approximations of JPEG have recently been proposed to addr
Externí odkaz:
http://arxiv.org/abs/2309.06978
Autor:
Reich, Christoph, Debnath, Biplob, Patel, Deep, Prangemeier, Tim, Cremers, Daniel, Chakradhar, Srimat
Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video code
Externí odkaz:
http://arxiv.org/abs/2308.16215
Autor:
Chang, Che-Jui, Li, Danrui, Patel, Deep, Goel, Parth, Zhou, Honglu, Moon, Seonghyeon, Sohn, Samuel S., Yoon, Sejong, Pavlovic, Vladimir, Kapadia, Mubbasir
The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world s
Externí odkaz:
http://arxiv.org/abs/2306.16772
Action recognition is an important problem that requires identifying actions in video by learning complex interactions across scene actors and objects. However, modern deep-learning based networks often require significant computation, and may captur
Externí odkaz:
http://arxiv.org/abs/2305.09539
Autor:
Khanna, Navneet, Patel, Deep, Raval, Parth, Airao, Jay, Badheka, Vishvesh, Rahman Rashid, Rizwan Abdul
Publikováno v:
In Tribology International December 2024 200
Autor:
Patel, Deep, Sastry, P. S.
Deep Neural Networks, often owing to the overparameterization, are shown to be capable of exactly memorizing even randomly labelled data. Empirical studies have also shown that none of the standard regularization techniques mitigate such overfitting.
Externí odkaz:
http://arxiv.org/abs/2107.09957
Data imbalance is a ubiquitous problem in machine learning. In large scale collected and annotated datasets, data imbalance is either mitigated manually by undersampling frequent classes and oversampling rare classes, or planned for with imputation a
Externí odkaz:
http://arxiv.org/abs/2107.03227
Autor:
Patel, Deep, Sastry, P. S.
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A prominent class
Externí odkaz:
http://arxiv.org/abs/2106.15292