1 code implementation • 4 Aug 2022 • Sudhakar Kumawat, Hajime Nagahara
This is followed by the Difference module to apply a pixel-wise intensity subtraction between consecutive frames to highlight motion features and suppress obvious high-level privacy attributes.
no code implementations • 17 Jun 2021 • Sudhakar Kumawat, Gagan Kanojia, Shanmuganathan Raman
This paper studies the operation of channel shuffle as a regularization technique in deep convolutional networks.
no code implementations • 22 Jul 2020 • Sudhakar Kumawat, Manisha Verma, Yuta Nakashima, Shanmuganathan Raman
To address these issues, we propose spatio-temporal short term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs.
1 code implementation • 22 Apr 2020 • Manisha Verma, Sudhakar Kumawat, Yuta Nakashima, Shanmuganathan Raman
To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes.
no code implementations • 27 Jan 2020 • Sudhakar Kumawat, Shanmuganathan Raman
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.
2 code implementations • 23 Nov 2019 • Davinder Singh, Naman jain, Pranjali Jain, Pratik Kayal, Sudhakar Kumawat, Nipun Batra
Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise.
no code implementations • 7 Sep 2019 • Gagan Kanojia, Sudhakar Kumawat, Shanmuganathan Raman
Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit.
no code implementations • 30 Apr 2019 • Gagan Kanojia, Sudhakar Kumawat, Shanmuganathan Raman
The proposed model outperforms the classification accuracy of the state-of-the-art models in both 2D and 3D frameworks by 11. 54% and 4. 24%, respectively.
no code implementations • 16 Apr 2019 • Sudhakar Kumawat, Manisha Verma, Shanmuganathan Raman
Recognizing facial expressions is one of the central problems in computer vision.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • CVPR 2019 • Sudhakar Kumawat, Shanmuganathan Raman
The ReLPV block extracts the phase in a 3D local neighborhood (e. g., 3x3x3) of each position of the input map to obtain the feature maps.