Search Results for author:
Found 10 papers, 3 papers with code
Privacy-Preserving Action Recognition via Motion Difference Quantization
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.
ShuffleBlock: Shuffle to Regularize Deep Convolutional Neural Networks
This paper studies the operation of channel shuffle as a regularization technique in deep convolutional networks.
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition
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.
Yoga-82: A New Dataset for Fine-grained Classification of Human Poses
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.
Depthwise-STFT based separable Convolutional Neural Networks
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
Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise.
Exploring Temporal Differences in 3D Convolutional Neural Networks
Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit.
Attentive Spatio-Temporal Representation Learning for Diving Classification
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.
LBVCNN: Local Binary Volume Convolutional Neural Network for Facial Expression Recognition from Image Sequences
Recognizing facial expressions is one of the central problems in computer vision.
Facial Expression Recognition
Facial Expression Recognition (FER)
LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks
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.
