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Found 5 papers, 0 papers with code
ShuffleBlock: Shuffle to Regularize Deep Convolutional Neural Networks
This paper studies the operation of channel shuffle as a regularization technique in deep convolutional networks.
Learning to Sort Image Sequences via Accumulated Temporal Differences
Let the temporal order in which these images are captured be unknown.
Simultaneous Detection and Removal of Dynamic Objects in Multi-view Images
During the scan, when a pixel is classified as dynamic, the proposed algorithm replaces that pixel value with the corresponding pixel value of the static region which is being occluded by that dynamic region.
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.
