Search Results for author:
Found 13 papers, 3 papers with code
Spatial Transform Decoupling for Oriented Object Detection
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks.
3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification.
Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces.
GAN-based Facial Attribute Manipulation
Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practical applications ranging from digital entertainment to biometric forensics.
A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks
Biphasic facial age translation aims at predicting the appearance of the input face at any age.
Style Intervention: How to Achieve Spatial Disentanglement with Style-based Generators?
Generative Adversarial Networks (GANs) with style-based generators (e. g. StyleGAN) successfully enable semantic control over image synthesis, and recent studies have also revealed that interpretable image translations could be obtained by modifying the latent code.
Reference-guided Face Component Editing
Face portrait editing has achieved great progress in recent years.
A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging
Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years.
Age Progression and Regression with Spatial Attention Modules
Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively.
Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
In this paper, we propose a deep multi-task learning framework, named as IrisParseNet, to exploit the inherent correlations between pupil, iris and sclera to boost up the performance of iris segmentation and localization in a unified model.
Ranked #1 on
Iris Segmentation
on CASIA
Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks
Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings.
Learning to Detect Human-Object Interactions
We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them.
Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection
This Estimation-Correction-Tuning process perfectly combines the advantages of the global robustness of data-driven method (FCN), outlier correction capability of model-driven method (PDM) and non-parametric optimization of RLMS.
