Search Results for author:
Found 23 papers, 6 papers with code
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Low-light image enhancement (LLIE) aims to improve low-illumination images.
StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face Synthesis
As cycle-like losses are designed to measure the L_2 distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i. e. no paired kinship face data is required.
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively.
Ranked #1 on
Medical Image Segmentation
on 2018 Data Science Bowl
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
Cross Language Image Matching for Weakly Supervised Semantic Segmentation
As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.
FEAT: Face Editing with Attention
In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation
As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.
Manifold-preserved GANs
Generative Adversarial Networks (GANs) have been widely adopted in various fields.
VHS to HDTV Video Translation using Multi-task Adversarial Learning
In this paper, we focus on the problem of translating VHS video to HDTV video and have developed a solution based on a novel unsupervised multi-task adversarial learning model.
GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing
Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic face editing.
Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network
Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection.
Class-Aware Domain Adaptation for Improving Adversarial Robustness
To improve the adversarial robustness of neural networks, adversarial training has been proposed to train networks by injecting adversarial examples into the training data.
Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
The extensive experiments and ablation study demonstrate the effectiveness of our approach on the domain adaptive segmentation task.
Learning Deep Image Priors for Blind Image Denoising
In this paper, we propose an effective image denoising method by learning two image priors from the perspective of domain alignment.
Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training
We present a new method for improving the performances of variational autoencoder (VAE).
Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification
In this paper, we present a novel deep hybrid attention approach to breast cancer classification.
End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks
In addition, we also contribute the first real world nature fog-fogfree image dataset for defogging research.
Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping
Building on crucial insights into the determining factors of the visual integrity of an image and the property of deep convolutional neural network (CNN), we have developed the Deep Feature Consistent Deep Image Transformation (DFC-DIT) framework which unifies challenging one-to-many mapping image processing problems such as image downscaling, decolorization (colour to grayscale conversion) and high dynamic range (HDR) image tone mapping.
Image Companding and Inverse Halftoning using Deep Convolutional Neural Networks
First, to the best knowledge of the authors, this is the first work that has successfully developed deep learning based solutions to these two traditional low-level image processing problems.
Deep Feature Consistent Variational Autoencoder
We present a novel method for constructing Variational Autoencoder (VAE).
Automatic Visual Theme Discovery from Joint Image and Text Corpora
Furthermore, not all tags have the same descriptive power for visual contents and large vocabulary available from natural language could result in a very diverse set of keywords.
Object Specific Deep Learning Feature and Its Application to Face Detection
We present a method for discovering and exploiting object specific deep learning features and use face detection as a case study.
