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Found 7 papers, 1 papers with code
User-assisted Video Reflection Removal
We show that user-assistance significantly improves the layer separation results.
Unsupervised Single-Image Reflection Separation Using Perceptual Deep Image Priors
Quantitative and qualitative results on commonly used datasets in the literature show that our method's performance is at least on par with the state-of-the-art supervised methods and, occasionally, better without requiring large training datasets.
Video Reflection Removal Through Spatio-Temporal Optimization
The novelty of our work is in our optimization formulation as well as the motion initialization strategy.
Efficient and Scalable View Generation from a Single Image using Fully Convolutional Networks
We present two FCN architectures for SIVG.
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks
Since current datasets are not large enough to train an accurate SBD CNN, we present a new dataset containing more than 3. 5 million frames of sharp and gradual transitions.
Video Magnification in Presence of Large Motions
Video magnification reveals subtle variations that would be otherwise invisible to the naked eye.
Analysis of PCA Algorithms in Distributed Environments
In this report, we analyze different methods for computing an important machine learing algorithm, namely Principal Component Analysis (PCA), and we comment on its limitations in supporting large datasets.
