Abstract We present Neural Head Avatars, a novel neural representation that explicitly
models the surface geometry and appearance of an animatable human avatar that can be …

In this paper, we contribute a new million-scale face benchmark containing noisy 4M
identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) …

Face reconstruction and tracking is a building block of numerous applications in AR/VR,
human-machine interaction, as well as medical applications. Most of these applications rely …

In a joint vision-language space, a text feature (eg, from" a photo of a dog") could effectively
represent its relevant image features (eg, from dog photos). Also, a recent study has …

One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …

Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets
and margin-based softmax loss is the current state-of-the-art approach for face recognition …

Generative adversarial networks (GANs) have been extensively studied in recent years and
have been used to address several problems in the fields of image generation and computer …

During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which
poses a huge challenge to deep face recognition. In this workshop, we organize Masked …

This paper presents an empirical study to efficiently build named entity recognition (NER)
systems when a small amount of in-domain labeled data is available. Based upon recent …

Although tremendous strides have been made in uncontrolled face detection, efficient face
detection with a low computation cost as well as high precision remains an open challenge …