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Found 10 papers, 5 papers with code
Video Frame Interpolation with Stereo Event and Intensity Camera
The stereo event-intensity camera setup is widely applied to leverage the advantages of both event cameras with low latency and intensity cameras that capture accurate brightness and texture information.
"Seeing'' Electric Network Frequency from Events
Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos.
Learning to Super-Resolve Blurry Images with Events
Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution.
"Seeing" Electric Network Frequency From Events
Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos.
Towards End-to-End Synthetic Speech Detection
The constant Q transform (CQT) has been shown to be one of the most effective speech signal pre-transforms to facilitate synthetic speech detection, followed by either hand-crafted (subband) constant Q cepstral coefficient (CQCC) feature extraction and a back-end binary classifier, or a deep neural network (DNN) directly for further feature extraction and classification.
Enhanced Time-Frequency Representation and Mode Decomposition
However, it is difficult for most previous methods to handle signal modes with closely-spaced or spectrally-overlapped instantaneous frequencies (IFs) especially in adverse environments.
Kernel Learning for High-Resolution Time-Frequency Distribution
To break the current limitation, we propose a data-driven kernel learning model directly based on Wigner-Ville distribution (WVD).
Robust Time-Frequency Reconstruction by Learning Structured Sparsity
Time-frequency distributions (TFDs) play a vital role in providing descriptive analysis of non-stationary signals involved in realistic scenarios.
Learning Cluster Structured Sparsity by Reweighting
Recently, the paradigm of unfolding iterative algorithms into finite-length feed-forward neural networks has achieved a great success in the area of sparse recovery.
Facial Feature Embedded CycleGAN for VIS-NIR Translation
VIS-NIR face recognition remains a challenging task due to the distinction between spectral components of two modalities and insufficient paired training data.
