Search Results for author: Yandong Wen

Found 18 papers, 6 papers with code

Self-Supervised 3D Face Reconstruction via Conditional Estimation

no code implementations ICCV 2021 Yandong Wen, Weiyang Liu, Bhiksha Raj, Rita Singh

We present a conditional estimation (CEST) framework to learn 3D facial parameters from 2D single-view images by self-supervised training from videos.

3D Face Reconstruction Disentanglement

SphereFace Revived: Unifying Hyperspherical Face Recognition

no code implementations12 Sep 2021 Weiyang Liu, Yandong Wen, Bhiksha Raj, Rita Singh, Adrian Weller

As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin.

Face Recognition

SphereFace2: Binary Classification is All You Need for Deep Face Recognition

no code implementations ICLR 2022 Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, Rita Singh

In this paper, we start by identifying the discrepancy between training and evaluation in the existing multi-class classification framework and then discuss the potential limitations caused by the "competitive" nature of softmax normalization.

Classification Face Recognition +1

MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement

2 code implementations ICCV 2021 Alexander Richard, Michael Zollhoefer, Yandong Wen, Fernando de la Torre, Yaser Sheikh

To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face.

3D Face Animation Disentanglement +1

Face Reconstruction from Voice using Generative Adversarial Networks

1 code implementation NeurIPS 2019 Yandong Wen, Bhiksha Raj, Rita Singh

The network learns to generate faces from voices by matching the identities of generated faces to those of the speakers, on a training set.

Face Reconstruction

Reconstructing faces from voices

1 code implementation25 May 2019 Yandong Wen, Rita Singh, Bhiksha Raj

Voice profiling aims at inferring various human parameters from their speech, e. g. gender, age, etc.

Optimal Strategies for Matching and Retrieval Problems by Comparing Covariates

no code implementations12 Jul 2018 Yandong Wen, Mahmoud Al Ismail, Bhiksha Raj, Rita Singh

In many retrieval problems, where we must retrieve one or more entries from a gallery in response to a probe, it is common practice to learn to do by directly comparing the probe and gallery entries to one another.

Disjoint Mapping Network for Cross-modal Matching of Voices and Faces

no code implementations ICLR 2019 Yandong Wen, Mahmoud Al Ismail, Weiyang Liu, Bhiksha Raj, Rita Singh

We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces.

Range Loss for Deep Face Recognition With Long-Tailed Training Data

no code implementations ICCV 2017 Xiao Zhang, Zhiyuan Fang, Yandong Wen, Zhifeng Li, Yu Qiao

Unlike these work, this paper investigated how long-tailed data impact the training of face CNNs and develop a novel loss function, called range loss, to effectively utilize the tailed data in training process.

Face Recognition

SphereFace: Deep Hypersphere Embedding for Face Recognition

16 code implementations CVPR 2017 Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.

Face Identification Face Recognition +1

Large-Margin Softmax Loss for Convolutional Neural Networks

2 code implementations7 Dec 2016 Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang

Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs).

General Classification

Range Loss for Deep Face Recognition with Long-tail

2 code implementations28 Nov 2016 Xiao Zhang, Zhiyuan Fang, Yandong Wen, Zhifeng Li, Yu Qiao

Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities.

Face Recognition

Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition

no code implementations CVPR 2016 Yandong Wen, Zhifeng Li, Yu Qiao

In order to address this problem, we propose a novel deep face recognition framework to learn the age-invariant deep face features through a carefully designed CNN model.

Age-Invariant Face Recognition

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