Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Kumawat, Sudhakar"'
Autor:
Kumawat, Sudhakar, Nagahara, Hajime
The widespread use of smart computer vision systems in our personal spaces has led to an increased consciousness about the privacy and security risks that these systems pose. On the one hand, we want these systems to assist in our daily lives by unde
Externí odkaz:
http://arxiv.org/abs/2208.02459
Deep neural networks have enormous representational power which leads them to overfit on most datasets. Thus, regularizing them is important in order to reduce overfitting and enhance their generalization capabilities. Recently, channel shuffle opera
Externí odkaz:
http://arxiv.org/abs/2106.09358
Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their feature learning capabilities. To address these issues, we propose spat
Externí odkaz:
http://arxiv.org/abs/2007.11365
Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for the learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion, and viewpoints. This makes
Externí odkaz:
http://arxiv.org/abs/2004.10362
In this paper, we propose a new convolutional layer called Depthwise-STFT Separable layer that can serve as an alternative to the standard depthwise separable convolutional layer. The construction of the proposed layer is inspired by the fact that th
Externí odkaz:
http://arxiv.org/abs/2001.09912
India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scala
Externí odkaz:
http://arxiv.org/abs/1911.10317
Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit. On the other hand, 2D CNNs are less computationally expensive and less memory intensive than 3D CNNs and h
Externí odkaz:
http://arxiv.org/abs/1909.03309
Competitive diving is a well recognized aquatic sport in which a person dives from a platform or a springboard into the water. Based on the acrobatics performed during the dive, diving is classified into a finite set of action classes which are stand
Externí odkaz:
http://arxiv.org/abs/1905.00050
Recognizing facial expressions is one of the central problems in computer vision. Temporal image sequences have useful spatio-temporal features for recognizing expressions. In this paper, we propose a new 3D Convolution Neural Network (CNN) that can
Externí odkaz:
http://arxiv.org/abs/1904.07647
Traditional 3D Convolutional Neural Networks (CNNs) are computationally expensive, memory intensive, prone to overfit, and most importantly, there is a need to improve their feature learning capabilities. To address these issues, we propose Rectified
Externí odkaz:
http://arxiv.org/abs/1904.03498