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Found 12 papers, 6 papers with code
WaveDH: Wavelet Sub-bands Guided ConvNet for Efficient Image Dehazing
In this paper, we introduce WaveDH, a novel and compact ConvNet designed to address this efficiency gap in image dehazing.
SuperStyleNet: Deep Image Synthesis with Superpixel Based Style Encoder
First, we directly extract the style codes from the original image based on superpixels to consider local objects.
Edge and Identity Preserving Network for Face Super-Resolution
In this paper, we propose a novel Edge and Identity Preserving Network for Face SR Network, named as EIPNet, to minimize the distortion by utilizing a lightweight edge block and identity information.
Frequency Pooling: Shift-Equivalent and Anti-Aliasing Down Sampling
Convolutional layer utilizes the shift-equivalent prior of images which makes it a great success for image processing.
GBDT-MO: Gradient Boosted Decision Trees for Multiple Outputs
When there are multiple outputs, GBDT constructs multiple trees corresponding to the output variables.
Adversarial Defense by Suppressing High-frequency Components
Recent works show that deep neural networks trained on image classification dataset bias towards textures.
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution
Although deep convolutional neural networks (CNNs) have obtained outstanding performance in image superresolution (SR), their computational cost increases geometrically as CNN models get deeper and wider.
Recurrent Convolution for Compact and Cost-Adjustable Neural Networks: An Empirical Study
However, the performance of an RC network is not satisfactory if we directly unroll the same kernels multiple steps.
Recurrent Convolutions: A Model Compression Point of View
RC reduces the redundancy across layers and is complementary to most existing model compression approaches.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment
Pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, and there exists the proposal shift problem in pedestrian detection that causes the loss of body parts such as head and legs.
Ranked #27 on
Pedestrian Detection
on Caltech
Regularization Neural Networks via Constrained Virtual Movement Field
We define the approximated smoothness as the regularization term.
