Search Results for author: Xiaobo Wang

Found 24 papers, 9 papers with code

Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition

no code implementations ECCV 2020 Xiaobo Wang, Tianyu Fu, Shengcai Liao, Shuo Wang, Zhen Lei, Tao Mei

Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.

Face Recognition Knowledge Distillation +1

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

no code implementations25 Jul 2021 Qiang Meng, Xiaqing Xu, Xiaobo Wang, Yang Qian, Yunxiao Qin, Zezheng Wang, Chenxu Zhao, Feng Zhou, Zhen Lei

Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e. g., in cases of surveillance and photo-tagging).

Face Recognition

An Efficient Training Approach for Very Large Scale Face Recognition

1 code implementation CVPR 2022 Kai Wang, Shuo Wang, Panpan Zhang, Zhipeng Zhou, Zheng Zhu, Xiaobo Wang, Xiaojiang Peng, Baigui Sun, Hao Li, Yang You

This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.

 Ranked #1 on Face Verification on IJB-C (training dataset metric)

Face Recognition Face Verification

Large Scale Product Graph Construction for Recommendation in E-commerce

1 code implementation12 Oct 2020 Xiaoyong Yang, Yadong Zhu, Yi Zhang, Xiaobo Wang, Quan Yuan

Building a recommendation system that serves billions of users on daily basis is a challenging problem, as the system needs to make astronomical number of predictions per second based on real-time user behaviors with O(1) time complexity.

Clustering graph construction +2

Weakly Supervised Construction of ASR Systems with Massive Video Data

no code implementations4 Aug 2020 Mengli Cheng, Chengyu Wang, Xu Hu, Jun Huang, Xiaobo Wang

Building Automatic Speech Recognition (ASR) systems from scratch is significantly challenging, mostly due to the time-consuming and financially-expensive process of annotating a large amount of audio data with transcripts.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Loss Function Search for Face Recognition

1 code implementation ICML 2020 Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei

In face recognition, designing margin-based (e. g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.

AutoML Face Recognition

A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing

no code implementations Proceedings of the AAAI Conference on Artificial Intelligence 2020 Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei

The dataset is publicly accessible to the community for boosting the advance of face parsing. 1 Second, a simple yet effective Boundary-Attention Semantic Segmentation (BASS) method is proposed for face parsing, which contains a three-branch network with elaborately developed loss functions to fully exploit the boundary information.

Face Parsing Image Generation +1

Mis-classified Vector Guided Softmax Loss for Face Recognition

no code implementations26 Nov 2019 Xiaobo Wang, Shifeng Zhang, Shuo Wang, Tianyu Fu, Hailin Shi, Tao Mei

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.

Face Recognition

Co-Mining: Deep Face Recognition With Noisy Labels

no code implementations ICCV 2019 Xiaobo Wang, Shuo Wang, Jun Wang, Hailin Shi, Tao Mei

Face recognition has achieved significant progress with the growing scale of collected datasets, which empowers us to train strong convolutional neural networks (CNNs).

Face Recognition Selection bias

A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark

no code implementations13 May 2019 Yinglu Liu, Hailin Shi, Yue Si, Hao Shen, Xiaobo Wang, Tao Mei

Each image is provided with accurate annotation of a 11-category pixel-level label map along with coordinates of 106-point landmarks.

Face Alignment Face Detection +3

Grand Challenge of 106-Point Facial Landmark Localization

no code implementations9 May 2019 Yinglu Liu, Hao Shen, Yue Si, Xiaobo Wang, Xiangyu Zhu, Hailin Shi, Zhibin Hong, Hanqi Guo, Ziyuan Guo, Yanqin Chen, Bi Li, Teng Xi, Jun Yu, Haonian Xie, Guochen Xie, Mengyan Li, Qing Lu, Zengfu Wang, Shenqi Lai, Zhenhua Chai, Xiaoming Wei

However, previous competitions on facial landmark localization (i. e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components.

Face Alignment Face Recognition +2

Prediction-Tracking-Segmentation

no code implementations5 Apr 2019 Jianren Wang, Yihui He, Xiaobo Wang, Xinjia Yu, Xia Chen

We introduce a prediction driven method for visual tracking and segmentation in videos.

Segmentation Video Segmentation +2

Improved Selective Refinement Network for Face Detection

no code implementations20 Jan 2019 Shifeng Zhang, Rui Zhu, Xiaobo Wang, Hailin Shi, Tianyu Fu, Shuo Wang, Tao Mei, Stan Z. Li

With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.

Data Augmentation Face Detection +1

Support Vector Guided Softmax Loss for Face Recognition

3 code implementations29 Dec 2018 Xiaobo Wang, Shuo Wang, Shifeng Zhang, Tianyu Fu, Hailin Shi, Tao Mei

Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.

Face Recognition

A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing

2 code implementations CVPR 2019 Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li

To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.

Face Anti-Spoofing Face Recognition

ScratchDet: Training Single-Shot Object Detectors from Scratch

1 code implementation CVPR 2019 Rui Zhu, Shifeng Zhang, Xiaobo Wang, Longyin Wen, Hailin Shi, Liefeng Bo, Tao Mei

Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence.

General Classification Object +2

Multi-Source Pointer Network for Product Title Summarization

no code implementations21 Aug 2018 Fei Sun, Peng Jiang, Hanxiao Sun, Changhua Pei, Wenwu Ou, Xiaobo Wang

For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.

Sentence Sentence Summarization

S3FD: Single Shot Scale-Invariant Face Detector

no code implementations ICCV 2017 Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.

Face Detection

S$^3$FD: Single Shot Scale-invariant Face Detector

3 code implementations17 Aug 2017 Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.

Face Detection

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

10 code implementations17 Aug 2017 Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales.

Face Detection Vocal Bursts Intensity Prediction

Deep Person Re-Identification with Improved Embedding and Efficient Training

1 code implementation9 May 2017 Haibo Jin, Xiaobo Wang, Shengcai Liao, Stan Z. Li

However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows.

Person Re-Identification

Adaptively Unified Semi-Supervised Dictionary Learning With Active Points

no code implementations ICCV 2015 Xiaobo Wang, Xiaojie Guo, Stan Z. Li

In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously.

Dictionary Learning

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