Search Results for author: Haifeng Liu

Found 31 papers, 16 papers with code

Reducing Idleness in Financial Cloud via Multi-objective Evolutionary Reinforcement Learning based Load Balancer

1 code implementation5 May 2023 Peng Yang, Laoming Zhang, Haifeng Liu, Guiying Li

In recent years, various companies started to shift their data services from traditional data centers onto cloud.

Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend

no code implementations6 Feb 2023 Ning Lu, Shengcai Liu, Zhirui Zhang, Qi Wang, Haifeng Liu, Ke Tang

Intuitively, this finding suggests a natural way to improve model robustness by training the model on the $n$-FD examples.

Adversarial Attack

Boosting Semi-Supervised 3D Object Detection with Semi-Sampling

no code implementations14 Nov 2022 Xiaopei Wu, Yang Zhao, Liang Peng, Hua Chen, Xiaoshui Huang, Binbin Lin, Haifeng Liu, Deng Cai, Wanli Ouyang

When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.

3D Object Detection Data Augmentation +1

Convolutional Embedding Makes Hierarchical Vision Transformer Stronger

no code implementations27 Jul 2022 Cong Wang, Hongmin Xu, Xiong Zhang, Li Wang, Zhitong Zheng, Haifeng Liu

Vision Transformers (ViTs) have recently dominated a range of computer vision tasks, yet it suffers from low training data efficiency and inferior local semantic representation capability without appropriate inductive bias.

Inductive Bias

DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection

1 code implementation18 Jul 2022 Liang Peng, Xiaopei Wu, Zheng Yang, Haifeng Liu, Deng Cai

Therefore, we propose to reformulate the instance depth to the combination of the instance visual surface depth (visual depth) and the instance attribute depth (attribute depth).

Data Augmentation Depth Estimation +3

Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion

1 code implementation CVPR 2022 Xiaopei Wu, Liang Peng, Honghui Yang, Liang Xie, Chenxi Huang, Chengqi Deng, Haifeng Liu, Deng Cai

Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance.

3D Object Detection Data Augmentation +3

Lidar Point Cloud Guided Monocular 3D Object Detection

1 code implementation19 Apr 2021 Liang Peng, Fei Liu, Zhengxu Yu, Senbo Yan, Dan Deng, Zheng Yang, Haifeng Liu, Deng Cai

We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels.

Monocular 3D Object Detection object-detection

Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

no code implementations29 Jan 2021 Hao Feng, Minghao Chen, Jinming Hu, Dong Shen, Haifeng Liu, Deng Cai

In this paper, to complement these low recall neighbor pseudo labels, we propose a joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels.

Person Re-Identification Unsupervised Domain Adaptation

Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework

no code implementations10 Oct 2020 Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, Wei Liu

Specifically, it first casts the relationships between a certain model's accuracy and depth/width/resolution into a polynomial regression and then maximizes the polynomial to acquire the optimal values for the three dimensions.

Network Pruning Neural Architecture Search +1

Class2Simi: A New Perspective on Learning with Label Noise

no code implementations28 Sep 2020 Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

It is worthwhile to perform the transformation: We prove that the noise rate for the noisy similarity labels is lower than that of the noisy class labels, because similarity labels themselves are robust to noise.

Socially-Aware Conference Participant Recommendation with Personality Traits

no code implementations9 Aug 2020 Feng Xia, Nana Yaw Asabere, Haifeng Liu, Zhen Chen, Wei Wang

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.

Recommendation Systems

Part-dependent Label Noise: Towards Instance-dependent Label Noise

1 code implementation NeurIPS 2020 Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, DaCheng Tao, Masashi Sugiyama

Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise.

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels

no code implementations14 Jun 2020 Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

To give an affirmative answer, in this paper, we propose a framework called Class2Simi: it transforms data points with noisy class labels to data pairs with noisy similarity labels, where a similarity label denotes whether a pair shares the class label or not.

Contrastive Learning Learning with noisy labels +1

Multi-Class Classification from Noisy-Similarity-Labeled Data

no code implementations16 Feb 2020 Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

We further estimate the transition matrix from only noisy data and build a novel learning system to learn a classifier which can assign noise-free class labels for instances.

Classification General Classification +1

Adversarial-Learned Loss for Domain Adaptation

1 code implementation4 Jan 2020 Minghao Chen, Shuai Zhao, Haifeng Liu, Deng Cai

In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA).

Domain Adaptation Pseudo Label

DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

no code implementations21 Dec 2019 Wenxiao Wang, Shuai Zhao, Minghao Chen, Jinming Hu, Deng Cai, Haifeng Liu

The dominant pruning methods, filter-level pruning methods, evaluate their performance through the reduction ratio of computations and deem that a higher reduction ratio of computations is equivalent to a higher acceleration ratio in terms of inference time.

Network Pruning

CFS: A Distributed File System for Large Scale Container Platforms

3 code implementations8 Nov 2019 Haifeng Liu, Wei Ding, Yu-An Chen, Weilong Guo, Shuoran Liu, Tianpeng Li, Mofei Zhang, Jianxing Zhao, Hongyin Zhu, Zhengyi Zhu

We propose CFS, a distributed file system for large scale container platforms.

Distributed, Parallel, and Cluster Computing

Training-Time-Friendly Network for Real-Time Object Detection

6 code implementations2 Sep 2019 Zili Liu, Tu Zheng, Guodong Xu, Zheng Yang, Haifeng Liu, Deng Cai

Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy.

object-detection Real-Time Object Detection

The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform

1 code implementation19 Aug 2019 Jie Li, Haifeng Liu, Chuanghua Gui, Jianyu Chen, Zhenyun Ni, Ning Wang

We present the design and implementation of a visual search system for real time image retrieval on JD. com, the world's third largest and China's largest e-commerce site.

Image Retrieval Retrieval

Addressing the Item Cold-start Problem by Attribute-driven Active Learning

no code implementations23 May 2018 Yu Zhu, Jinhao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai

Both content information (e. g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item.

Active Learning Collaborative Filtering +1

PixelLink: Detecting Scene Text via Instance Segmentation

5 code implementations4 Jan 2018 Dan Deng, Haifeng Liu, Xuelong. Li, Deng Cai

Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression.

Instance Segmentation regression +4

On the Diversity of Realistic Image Synthesis

1 code implementation20 Dec 2017 Zichen Yang, Haifeng Liu, Deng Cai

Experimental results show that images synthesized by our approach are significantly more diverse than that of the current existing works and equipping our diversity loss does not degrade the reality of the base networks.

Colorization Image Generation +1

BENCHIP: Benchmarking Intelligence Processors

no code implementations23 Oct 2017 Jinhua Tao, Zidong Du, Qi Guo, Huiying Lan, Lei Zhang, Shengyuan Zhou, Lingjie Xu, Cong Liu, Haifeng Liu, Shan Tang, Allen Rush, Willian Chen, Shaoli Liu, Yunji Chen, Tianshi Chen

The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware).


Deep Rotation Equivariant Network

2 code implementations24 May 2017 Junying Li, Zichen Yang, Haifeng Liu, Deng Cai

Recently, learning equivariant representations has attracted considerable research attention.

Rotated MNIST

Separable Kernel for Image Deblurring

no code implementations CVPR 2014 Lu Fang, Haifeng Liu, Feng Wu, Xiaoyan Sun, Houqiang Li

In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system.

Deblurring Image Deblurring

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