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Found 7 papers, 1 papers with code
Reconstructing the Noise Variance Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
Poly-NL: Linear Complexity Non-local Layers with Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Ranked #1 on
Face Detection
on WIDER Face (Hard)
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Reconstructing the Noise Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
Pixel Adaptive Filtering Units
Next, we employ PAFU in deep neural networks as a replacement of standard convolutional layers to enhance the original architectures with spatially varying computations to achieve considerable performance improvements.
Deep Globally Constrained MRFs for Human Pose Estimation
We overcome this by introducing a Markov Random Field (MRF)-based spatial model network between the coarse and the refinement model that introduces geometric constraints on the relative locations of the body joints.
