Search Results for author:
Found 21 papers, 4 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.
Tripartite Information Mining and Integration for Image Matting
Regarding the nature of image matting, most researches have focused on solutions for transition regions.
Referring Image Segmentation Using Text Supervision
Hence, we propose a novel weakly-supervised RIS framework to formulate the target localization problem as a classification process to differentiate between positive and negative text expressions.
Gaussian Process-Gated Hierarchical Mixtures of Experts
Unlike other mixtures of experts with gating models linear in the input, our model employs gating functions built with Gaussian processes (GPs).
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.
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.
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.
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.
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.
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.
Structure-Informed Shadow Removal Networks
Hence, in this paper, we propose to remove shadows at the image structure level.
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.
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.
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.
NeRT: Implicit Neural Representations for General Unsupervised Turbulence Mitigation
The atmospheric and water turbulence mitigation problems have emerged as challenging inverse problems in computer vision and optics communities over the years.
Digital Twin Brain: a simulation and assimilation platform for whole human brain
In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive.
ISLAND: Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator
Our approach uses thermal infrared images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset.
Recasting Regional Lighting for Shadow Removal
Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region.
Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis.
Decentralizing Coherent Joint Transmission Precoding via Deterministic Equivalents
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission.
Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents
Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme.
