Search Results for author: Shifeng Zhang

Found 31 papers, 11 papers with code

Split Hierarchical Variational Compression

no code implementations5 Apr 2022 Tom Ryder, Chen Zhang, Ning Kang, Shifeng Zhang

Secondly, we define our coding framework, the autoregressive initial bits, that flexibly supports parallel coding and avoids -- for the first time -- many of the practicalities commonly associated with bits-back coding.

Image Compression

Memory Replay with Data Compression for Continual Learning

1 code implementation ICLR 2022 Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu

In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.

Autonomous Driving class-incremental learning +4

OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression

no code implementations NeurIPS 2021 Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li

To eliminate the requirement of saving separate models for different target datasets, we propose a novel setting that starts from a pretrained deep generative model and compresses the data batches while adapting the model with a dynamical system for only one epoch.

Density Estimation

iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder

no code implementations NeurIPS 2021 Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li

In this paper, we discuss lossless compression using normalizing flows which have demonstrated a great capacity for achieving high compression ratios.

Nonlinear ICA Using Volume-Preserving Transformations

no code implementations ICLR 2022 Xiaojiang Yang, Yi Wang, Jiacheng Sun, Xing Zhang, Shifeng Zhang, Zhenguo Li, Junchi Yan

Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources.

Understanding and Exploring the Network with Stochastic Architectures

no code implementations NeurIPS 2020 Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu

In this work, we decouple the training of a network with stochastic architectures (NSA) from NAS and provide a first systematical investigation on it as a stand-alone problem.

Neural Architecture Search

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

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

10 code implementations CVPR 2020 Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, Stan Z. Li

In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.

Object Detection

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

WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

no code implementations25 Sep 2019 Shifeng Zhang, Yiliang Xie, Jun Wan, Hansheng Xia, Stan Z. Li, Guodong Guo

To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild.

Ranked #3 on Object Detection on WiderPerson (mMR metric)

Object Detection Pedestrian Detection

Relational Learning for Joint Head and Human Detection

no code implementations24 Sep 2019 Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou

Head and human detection have been rapidly improved with the development of deep convolutional neural networks.

Head Detection Human Detection +1

PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

no code implementations15 Sep 2019 Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou

Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians.

Data Augmentation Occlusion Handling +2

DARTS+: Improved Differentiable Architecture Search with Early Stopping

no code implementations13 Sep 2019 Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, Zhenguo Li

Therefore, we propose a simple and effective algorithm, named "DARTS+", to avoid the collapse and improve the original DARTS, by "early stopping" the search procedure when meeting a certain criterion.

RefineFace: Refinement Neural Network for High Performance Face Detection

no code implementations10 Sep 2019 Shifeng Zhang, Cheng Chi, Zhen Lei, Stan Z. Li

To improve the classification ability for high recall efficiency, STC first filters out most simple negatives from low level detection layers to reduce search space for subsequent classifier, then SML is applied to better distinguish faces from background at various scales and FSM is introduced to let the backbone learn more discriminative features for classification.

Face Detection General Classification

CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

no code implementations28 Aug 2019 Shifeng Zhang, Ajian Liu, Jun Wan, Yanyan Liang, Guogong Guo, Sergio Escalera, Hugo Jair Escalante, 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 modalities.

Face Anti-Spoofing Face Recognition

Pairwise Teacher-Student Network for Semi-Supervised Hashing

no code implementations2 Feb 2019 Shifeng Zhang, Jianmin Li, Bo Zhang

Hashing method maps similar high-dimensional data to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low storage cost and fast retrieval speed.

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 Detection

Selective Refinement Network for High Performance Face Detection

3 code implementations7 Sep 2018 Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou

In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.

Face Detection General Classification

Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

no code implementations ECCV 2018 Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other.

Ranked #8 on Pedestrian Detection on Caltech (using extra training data)

Pedestrian Detection

Semantic Cluster Unary Loss for Efficient Deep Hashing

1 code implementation15 May 2018 Shifeng Zhang, Jianmin Li, Bo Zhang

The resultant hashcodes form several compact clusters, which means hashcodes in the same cluster have similar semantic information.

Information Retrieval

Single-Shot Refinement Neural Network for Object Detection

10 code implementations CVPR 2018 Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li

For object detection, the two-stage approach (e. g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e. g., SSD) has the advantage of high efficiency.

Object Detection

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

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

8 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

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

Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization

no code implementations28 Sep 2016 Shifeng Zhang, Jianmin Li, Jinma Guo, Bo Zhang

Hashing method maps similar data to binary hashcodes with smaller hamming distance, and it has received a broad attention due to its low storage cost and fast retrieval speed.

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