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Found 13 papers, 3 papers with code
Neural Preset for Color Style Transfer
In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed.
Gaussian Process-Gated Hierarchical Mixtures of Experts
Further, the experts are also built with Gaussian processes and provide predictions that depend on test data.
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular values.
Sequential Estimation of Gaussian Process-based Deep State-Space Models
We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes of the models.
Data-driven intelligent computational design for products: Method, techniques, and applications
Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence.
Structure-Informed Shadow Removal Networks
Extensive experiments on three shadow removal benchmarks demonstrate that our method outperforms existing shadow removal methods, and our StructNet can be integrated with existing methods to boost their performances further.
Hierarchical and Progressive Image Matting
In this paper, we propose an end-to-end Hierarchical and Progressive Attention Matting Network (HAttMatting++), which can better predict the opacity of the foreground from single RGB images without additional input.
Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting
This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting.
Prior-Induced Information Alignment for Image Matting
Image matting is an ill-posed problem that aims to estimate the opacity of foreground pixels in an image.
Multi-scale Information Assembly for Image Matting
Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images.
Tripartite Information Mining and Integration for Image Matting
Regarding the nature of image matting, most researches have focused on solutions for transition regions.
Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors.
Deep Structured Models For Group Activity Recognition
This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes.
