no code implementations • 27 Jun 2023 • Santosh Kumar Yadav, Apurv Shukla, Kamlesh Tiwari, Hari Mohan Pandey, Shaik Ali Akbar
The proposed model consists of four steps as follows: (a) first, the region of interest (ROI) is segmented using segmentation based approaches to extract the ROI from the original images; (b) second, these refined images are passed to a CNN architecture based on the backbone of EfficientNets for feature extraction; (c) third, dense refinement blocks, adapted from the architecture of densely connected networks are added to learn more diversified features; and (d) fourth, global average pooling and fully connected layers are applied for the classification of the multi-level hierarchy of the yoga poses.
no code implementations • 27 Jun 2023 • Santosh Kumar Yadav, Muhtashim Rafiqi, Egna Praneeth Gummana, Kamlesh Tiwari, Hari Mohan Pandey, Shaik Ali Akbara
In the second stream, data obtained from inertial sensors are pre-processed and inputted to regularized LSTMs for the feature extraction followed by fully connected layers for the classification.
no code implementations • 7 Dec 2022 • Santosh Kumar Yadav, Achleshwar Luthra, Esha Pahwa, Kamlesh Tiwari, Heena Rathore, Hari Mohan Pandey, Peter Corcoran
To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention.
no code implementations • 10 Nov 2022 • Santosh Kumar Yadav, Esha Pahwa, Achleshwar Luthra, Kamlesh Tiwari, Hari Mohan Pandey, Peter Corcoran
To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Fusion (SWTF) module to utilize sparsely sampled video frames for obtaining global weighted temporal fusion outcome.
1 code implementation • 12 Feb 2020 • Jia Qian, Lars Kai Hansen, Xenofon Fafoutis, Prayag Tiwari, Hari Mohan Pandey
Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge.