Search Results for author:
Found 7 papers, 3 papers with code
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution
In particular, we integrate both CNN and Transformer components into the SR network, where we first use the CNN modulated by the degradation information to extract local features, and then employ the degradation-aware Transformer to extract global semantic features.
Edit-DiffNeRF: Editing 3D Neural Radiance Fields using 2D Diffusion Model
To address this challenge, we propose the Edit-DiffNeRF framework, which is composed of a frozen diffusion model, a proposed delta module to edit the latent semantic space of the diffusion model, and a NeRF.
Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis
Instead of encoding view directions to model view-dependent effects, we further propose to encode the rendering equation in the feature space by employing the anisotropic spherical Gaussian mixture predicted from the proposed multiscale representation.
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation
However, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SR images reconstructed from authentic LR images.
Multi-Scale Fully Convolutional Network for Cardiac Left Ventricle Segmentation
Compared with traditional methods, the segmentation algorithms based on fully convolutional neural network greatly improve the accuracy of semantic segmentation.
Deep Spatial Regression Model for Image Crowd Counting
Computer vision techniques have been used to produce accurate and generic crowd count estimators in recent years.
Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image.
