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Found 6 papers, 5 papers with code
Deep Attentive Time Warping
Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task.
Deep Quantigraphic Image Enhancement via Comparametric Equations
Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric.
Reflectance-Oriented Probabilistic Equalization for Image Enhancement
Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images.
Reflectance-Guided, Contrast-Accumulated Histogram Equalization
Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously.
Attention to Warp: Deep Metric Learning for Multivariate Time Series
This approach adapts a parameterized attention model to time warping for greater and more adaptive temporal invariance.
Adaptive Dither Voting for Robust Spatial Verification
Hough voting in a geometric transformation space allows us to realize spatial verification, but remains sensitive to feature detection errors because of the inflexible quantization of single feature correspondences.
