Search Results for author: Meina Kan

Found 20 papers, 7 papers with code

EigenGAN: Layer-Wise Eigen-Learning for GANs

1 code implementation ICCV 2021 Zhenliang He, Meina Kan, Shiguang Shan

Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations.

Face Generation

PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing

3 code implementations12 Jul 2020 Zhenliang He, Meina Kan, Jichao Zhang, Shiguang Shan

Facial attribute editing aims to manipulate attributes on the human face, e. g., adding a mustache or changing the hair color.

Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

2 code implementations CVPR 2020 Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen

Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation.

Data Augmentation Weakly-Supervised Semantic Segmentation

Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation

1 code implementation9 Sep 2019 Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen

This regularized CAM can be embedded in most recent advanced weakly supervised semantic segmentation framework.

Weakly-Supervised Semantic Segmentation

Fully Learnable Group Convolution for Acceleration of Deep Neural Networks

no code implementations CVPR 2019 Xijun Wang, Meina Kan, Shiguang Shan, Xilin Chen

Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e. g. smartphone, embedded devices, etc.

Meta-Learning with Individualized Feature Space for Few-Shot Classification

no code implementations27 Sep 2018 Chunrui Han, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen

Specifically, we introduce a kernel generator as meta-learner to learn to construct feature embedding for query images.

Classification Meta-Learning +1

Generative Adversarial Network with Spatial Attention for Face Attribute Editing

1 code implementation ECCV 2018 Gang Zhang, Meina Kan, Shiguang Shan, Xilin Chen

The generator contains an attribute manipulation network (AMN) to edit the face image, and a spatial attention network (SAN) to localize the attribute-specific region which restricts the alternation of AMN within this region.

Data Augmentation Face Recognition

Face Recognition with Contrastive Convolution

no code implementations ECCV 2018 Chunrui Han, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen

In current face recognition approaches with convolutional neural network (CNN), a pair of faces to compare are independently fed into the CNN for feature extraction.

Face Recognition Face Verification

Duplex Generative Adversarial Network for Unsupervised Domain Adaptation

no code implementations CVPR 2018 Lanqing Hu, Meina Kan, Shiguang Shan, Xilin Chen

Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation.

Object Recognition Unsupervised Domain Adaptation

Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

1 code implementation CVPR 2018 Xuepeng Shi, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen

Rotation-invariant face detection, i. e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances.

Face Detection

AttGAN: Facial Attribute Editing by Only Changing What You Want

8 code implementations29 Nov 2017 Zhenliang He, WangMeng Zuo, Meina Kan, Shiguang Shan, Xilin Chen

Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes.

Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition

no code implementations ICCV 2017 Wanglong Wu, Meina Kan, Xin Liu, Yi Yang, Shiguang Shan, Xilin Chen

The designed ReST has an intrinsic recursive structure and is capable of progressively aligning faces to a canonical one, even those with large variations.

Face Alignment Face Recognition

Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness

no code implementations23 Sep 2016 Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen

On the other hand, by using a unified MLP cascade to examine proposals of all views in a centralized style, it provides a favorable solution for multi-view face detection with high accuracy and low time-cost.

Face Alignment Face Detection

Occlusion-Free Face Alignment: Deep Regression Networks Coupled With De-Corrupt AutoEncoders

no code implementations CVPR 2016 Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen

Face alignment or facial landmark detection plays an important role in many computer vision applications, e. g., face recognition, facial expression recognition, face animation, etc.

Face Alignment Face Recognition +2

Multi-View Deep Network for Cross-View Classification

no code implementations CVPR 2016 Meina Kan, Shiguang Shan, Xilin Chen

As a result, the representation from the topmost layers of the MvDN network is robust to view discrepancy, and also discriminative.

Classification Face Recognition +1

Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation

no code implementations ICCV 2015 Meina Kan, Shiguang Shan, Xilin Chen

To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE).

Face Recognition Unsupervised Domain Adaptation

Leveraging Datasets With Varying Annotations for Face Alignment via Deep Regression Network

no code implementations ICCV 2015 Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen

Facial landmark detection, as a vital topic in computer vision, has been studied for many decades and lots of datasets have been collected for evaluation.

Face Alignment Facial Landmark Detection

Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses

no code implementations CVPR 2014 Meina Kan, Shiguang Shan, Hong Chang, Xilin Chen

Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity.

Face Recognition Pose Estimation

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