656 citations · 661 across the 5 of their papers we have counts for
8 papers
ShuffleBlock: Shuffle to Regularize Deep Convolutional Neural Networks
Sudhakar Kumawat, Gagan Kanojia, Shanmuganathan Raman
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 enha…
Yoga-82: A New Dataset for Fine-grained Classification of Human Poses
Manisha Verma, Sudhakar Kumawat, Yuta Nakashima +1
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…
Depthwise-STFT based separable Convolutional Neural Networks
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.…
PlantDoc: A Dataset for Visual Plant Disease Detection
Davinder Singh, Naman Jain, Pranjali Jain +3
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 pap…
Exploring Temporal Differences in 3D Convolutional Neural Networks
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. On the other hand, 2D CNNs are less…
Attentive Spatio-Temporal Representation Learning for Diving Classification
Gagan Kanojia, Sudhakar Kumawat, Shanmuganathan Raman
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, di…