no code implementations • 20 Mar 2023 • Xing Cui, Zekun Li, Peipei Li, Yibo Hu, Hailin Shi, Zhaofeng He
The dataset is constructed upon the CelebA-HQ dataset with images annotated with a multi-turn dialogue that corresponds to the user editing requirements.
no code implementations • 19 Sep 2022 • Hailin Shi, Hang Du, Yibo Hu, Jun Wang, Dan Zeng, Ting Yao
Such multi-shot scheme brings inference burden, and the predefined scales inevitably have gap from real data.
2 code implementations • 3 Mar 2022 • ZiCheng Zhang, Yinglu Liu, Congying Han, Hailin Shi, Tiande Guo, BoWen Zhou
Moreover, we apply our method to other image manipulation tasks (e. g., style transfer, harmonization), and the results further prove the effectiveness and efficiency of our method.
1 code implementation • 1 Dec 2021 • Hangtong Wu, Dan Zen, Yibo Hu, Hailin Shi, Tao Mei
Such noisy samples are hard to predict precise depth values, thus may obstruct the widely-used depth supervised optimization.
no code implementations • 7 Jul 2021 • Hanbin Dai, Hailin Shi, Wu Liu, Linfang Wang, Yinglu Liu, Tao Mei
By the experimental analysis, we find that the HR representation leads to a sharp increase of computational cost, while the accuracy improvement remains marginal compared with the low-resolution (LR) representation.
1 code implementation • 10 May 2021 • Yuchi Liu, Hailin Shi, Hang Du, Rui Zhu, Jun Wang, Liang Zheng, Tao Mei
This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling.
no code implementations • 10 May 2021 • Hailin Shi, Dan Zeng, Yichun Tai, Hang Du, Yibo Hu, ZiCheng Zhang, Tao Mei
However, unlike the existing public face datasets, in many real-world scenarios of face recognition, the depth of training dataset is shallow, which means only two face images are available for each ID.
no code implementations • 14 Apr 2021 • Hang Du, Hailin Shi, Yinglu Liu, Dan Zeng, Tao Mei
In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method.
1 code implementation • CVPR 2021 • Jiahui She, Yibo Hu, Hailin Shi, Jun Wang, Qiu Shen, Tao Mei
Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity.
1 code implementation • ICCV 2021 • Chaoyou Fu, Yibo Hu, Xiang Wu, Hailin Shi, Tao Mei, Ran He
Visible-Infrared person re-identification (VI-ReID) aims to match cross-modality pedestrian images, breaking through the limitation of single-modality person ReID in dark environment.
no code implementations • 18 Jan 2021 • Gusi Te, Wei Hu, Yinglu Liu, Hailin Shi, Tao Mei
Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently.
Ranked #3 on Face Parsing on CelebAMask-HQ
2 code implementations • 12 Jan 2021 • Jun Wang, Yinglu Liu, Yibo Hu, Hailin Shi, Tao Mei
For example, the production of face representation network desires a modular training scheme to consider the proper choice from various candidates of state-of-the-art backbone and training supervision subject to the real-world face recognition demand; for performance analysis and comparison, the standard and automatic evaluation with a bunch of models on multiple benchmarks will be a desired tool as well; besides, a public groundwork is welcomed for deploying the face recognition in the shape of holistic pipeline.
no code implementations • 28 Sep 2020 • Hang Du, Hailin Shi, Dan Zeng, Xiao-Ping Zhang, Tao Mei
To start with, we present an overview of the end-to-end deep face recognition.
1 code implementation • ECCV 2020 • Gusi Te, Yinglu Liu, Wei Hu, Hailin Shi, Tao Mei
Specifically, we encode a facial image onto a global graph representation where a collection of pixels ("regions") with similar features are projected to each vertex.
Ranked #4 on Face Parsing on CelebAMask-HQ
no code implementations • 20 Jul 2020 • Dan Zeng, Hailin Shi, Hang Du, Jun Wang, Zhen Lei, Tao Mei
However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits.
2 code implementations • ECCV 2020 • Hang Du, Hailin Shi, Yuchi Liu, Jun Wang, Zhen Lei, Dan Zeng, Tao Mei
Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.
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.
Ranked #8 on Face Parsing on LaPa
no code implementations • 26 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.
1 code implementation • International Conference on Computer Vision Workshops 2019 • Dawei Du, Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Lin, QinGhua Hu, Tao Peng, Jiayu Zheng, Xinyao Wang, Yue Zhang, Liefeng Bo, Hailin Shi, Rui Zhu, Aashish Kumar, Aijin Li, Almaz Zinollayev, Anuar Askergaliyev, Arne Schumann, Binjie Mao, Byeongwon Lee, Chang Liu, Changrui Chen, Chunhong Pan, Chunlei Huo, Da Yu, Dechun Cong, Dening Zeng, Dheeraj Reddy Pailla, Di Li, Dong Wang, Donghyeon Cho, Dongyu Zhang, Furui Bai, George Jose, Guangyu Gao, Guizhong Liu, Haitao Xiong, Hao Qi, Haoran Wang, Heqian Qiu, Hongliang Li, Huchuan Lu, Ildoo Kim, Jaekyum Kim, Jane Shen, Jihoon Lee, Jing Ge, Jingjing Xu, Jingkai Zhou, Jonas Meier, Jun Won Choi, Junhao Hu, Junyi Zhang, Junying Huang, Kaiqi Huang, Keyang Wang, Lars Sommer, Lei Jin, Lei Zhang
Results of 33 object detection algorithms are presented.
1 code implementation • 6 Aug 2019 • Chen Ma, Chenxu Zhao, Hailin Shi, Li Chen, Junhai Yong, Dan Zeng
To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples.
no code implementations • 13 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.
no code implementations • 9 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.
no code implementations • 20 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.
3 code implementations • 29 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.
Ranked #1 on Face Identification on Trillion Pairs Dataset
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.
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.
no code implementations • 8 Jun 2018 • Xiangyu Zhu, Hao liu, Zhen Lei, Hailin Shi, Fan Yang, Dong Yi, Guo-Jun Qi, Stan Z. Li
In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition.
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.
8 code implementations • 17 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.
Ranked #3 on Face Detection on PASCAL Face
3 code implementations • 17 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.
Ranked #2 on Face Detection on PASCAL Face
no code implementations • 1 Nov 2016 • Hailin Shi, Yang Yang, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Wei-Shi Zheng, Stan Z. Li
From this point of view, selecting suitable positive i. e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations.
no code implementations • 9 May 2016 • Hailin Shi, Xiangyu Zhu, Zhen Lei, Shengcai Liao, Stan Z. Li
Deep neural networks usually benefit from unsupervised pre-training, e. g. auto-encoders.
no code implementations • 24 Nov 2015 • Hailin Shi, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Yang Yang, Stan Z. Li
In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification.
no code implementations • CVPR 2016 • Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, Stan Z. Li
Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community.
Ranked #3 on 3D Face Reconstruction on Florence