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Found 7 papers, 2 papers with code
Towards Continual, Online, Self-Supervised Depth
We apply our method to both structure-from-motion and stereo depth estimation.
Increased-Range Unsupervised Monocular Depth Estimation
Small-baseline stereo pairs provide small depth range but handle occlusions well.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
Self-Supervised Representation Learning for Visual Anomaly Detection
The best performing image-based self-supervised representation learning method is then used for video anomaly detection to see the importance of spatial features in visual anomaly detection in videos.
Global Feature Aggregation for Accident Anticipation
Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance.
Fine-Tuning DARTS for Image Classification
Our method offers a good trade-off between the number of parameters and classification accuracy.
Ranked #1 on
Image Classification
on Fashion-MNIST
Plug-and-Play Anomaly Detection with Expectation Maximization Filtering
With a single epoch of training, our method improves the AUC by 8. 03% compared to the convolutional LSTM-based approach.
Efficient Neural Network Compression
The better accuracy and complexity compromise, as well as the extremely fast speed of our method makes it suitable for neural network compression.
