Search Results for author: Meina Kan

Found 25 papers, 9 papers with code

Triplet Knowledge Distillation

no code implementations25 May 2023 Xijun Wang, Dongyang Liu, Meina Kan, Chunrui Han, Zhongqin Wu, Shiguang Shan

Distillation then begins in an online manner, and the teacher is only allowed to express solutions within the aforementioned subspace.

Face Recognition Image Classification +1

Function-Consistent Feature Distillation

1 code implementation24 Apr 2023 Dongyang Liu, Meina Kan, Shiguang Shan, Xilin Chen

The core idea of FCFD is to make teacher and student features not only numerically similar, but more importantly produce similar outputs when fed to the later part of the same network.

Image Classification object-detection +1

DandelionNet: Domain Composition with Instance Adaptive Classification for Domain Generalization

no code implementations ICCV 2023 Lanqing Hu, Meina Kan, Shiguang Shan, Xilin Chen

Domain generalization (DG) attempts to learn a model on source domains that can well generalize to unseen but different domains.

Domain Generalization

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.

Attribute Face Generation +1

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.

Attribute Generative Adversarial Network

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 +1

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

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

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.

Attribute Data Augmentation +2

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.

Generative Adversarial Network Object Recognition +1

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.

Binary Classification Face Detection

AttGAN: Facial Attribute Editing by Only Changing What You Want

10 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.

Attribute

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

VIPLFaceNet: An Open Source Deep Face Recognition SDK

no code implementations13 Sep 2016 Xin Liu, Meina Kan, Wanglong Wu, Shiguang Shan, Xilin Chen

Robust face representation is imperative to highly accurate face recognition.

Face Recognition

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 +4

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 Test +1

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 +1

AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation

no code implementations ICCV Workshop 2015 Xin Liu, Shaoxin Li, Meina Kan, Jie Zhang, Shuzhe Wu, Wenxian Liu, Hu Han, Shiguang Shan, Xilin Chen

Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme.

Age Estimation Transfer Learning

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 +1

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