Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Desai, Kevin A."'
In this paper, we propose a novel method for monocular depth estimation in dynamic scenes. We first explore the arbitrariness of object's movement trajectory in dynamic scenes theoretically. To overcome the arbitrariness, we use assume that points mo
Externí odkaz:
http://arxiv.org/abs/2411.04227
Autor:
Setu, Jyotirmay Nag, Le, Joshua M, Kundu, Ripan Kumar, Giesbrecht, Barry, Höllerer, Tobias, Hoque, Khaza Anuarul, Desai, Kevin, Quarles, John
Virtual Reality (VR) is quickly establishing itself in various industries, including training, education, medicine, and entertainment, in which users are frequently required to carry out multiple complex cognitive and physical activities. However, th
Externí odkaz:
http://arxiv.org/abs/2409.06898
Image classification is a fundamental task in computer vision, and the quest to enhance DNN accuracy without inflating model size or latency remains a pressing concern. We make a couple of advances in this regard, leading to a novel EncodeNet design
Externí odkaz:
http://arxiv.org/abs/2404.13770
Autor:
Azam, Md Mushfiqur, Desai, Kevin
Egocentric human pose estimation aims to estimate human body poses and develop body representations from a first-person camera perspective. It has gained vast popularity in recent years because of its wide range of applications in sectors like XR-tec
Externí odkaz:
http://arxiv.org/abs/2403.17893
Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resource-constrained edge devices. To address this problem, we propose a novel approach based
Externí odkaz:
http://arxiv.org/abs/2403.07036
In the recent years, various 3D mixed reality serious games have been developed for different applications such as physical training, rehabilitation, and education. Task performance in a serious game is a measurement of how efficiently and accurately
Externí odkaz:
http://arxiv.org/abs/2302.05795
Virtual Reality (VR) sickness commonly known as cybersickness is one of the major problems for the comfortable use of VR systems. Researchers have proposed different approaches for predicting cybersickness from bio-physiological data (e.g., heart rat
Externí odkaz:
http://arxiv.org/abs/2108.06437
With the ever-increasing amount of data, the central challenge in multimodal learning involves limitations of labelled samples. For the task of classification, techniques such as meta-learning, zero-shot learning, and few-shot learning showcase the a
Externí odkaz:
http://arxiv.org/abs/2106.14082
Visual Question Answering (VQA) models have achieved significant success in recent times. Despite the success of VQA models, they are mostly black-box models providing no reasoning about the predicted answer, thus raising questions for their applicab
Externí odkaz:
http://arxiv.org/abs/2105.07141
Autor:
Krishna, Hema, Desai, Kevin, Slostad, Brody, Bhayani, Siddharth, Arnold, Joshua H., Ouwerkerk, Wouter, Hummel, Yoran, Lam, Carolyn S.P., Ezekowitz, Justin, Frost, Matthew, Jiang, Zhubo, Equilbec, Cyril, Twing, Aamir, Pellikka, Patricia A., Frazin, Leon, Kansal, Mayank
Publikováno v:
In Journal of the American Society of Echocardiography July 2023 36(7):769-777
