Search Results for author: Jianguo Li

Found 27 papers, 15 papers with code

BALANCE: Bayesian Linear Attribution for Root Cause Localization

1 code implementation31 Jan 2023 Chaoyu Chen, Hang Yu, Zhichao Lei, Jianguo Li, Shaokang Ren, Tingkai Zhang, Silin Hu, Jianchao Wang, Wenhui Shi

In particular, we propose BALANCE (BAyesian Linear AttributioN for root CausE localization), which formulates the problem of RCA through the lens of attribution in XAI and seeks to explain the anomalies in the target KPIs by the behavior of the candidate root causes.

Fault Detection Fault localization +1

Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting

1 code implementation12 Oct 2022 Hongyuan Yu, Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, Alex Liu

In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure.

Graph Generation Graph structure learning +1

A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

1 code implementation31 May 2022 Siqiao Xue, Chao Qu, Xiaoming Shi, Cong Liao, Shiyi Zhu, Xiaoyu Tan, Lintao Ma, Shiyu Wang, Shijun Wang, Yun Hu, Lei Lei, Yangfei Zheng, Jianguo Li, James Zhang

Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.

Decision Making Management +3

Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis

no code implementations7 Apr 2022 He Zhou, Haibo Zhou, Jianguo Li, Kai Yang, Jianping An, Xuemin, Shen

By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived.

Point Processes

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

1 code implementation ICLR 2022 Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, Schahram Dustdar

Accurate prediction of the future given the past based on time series data is of paramount importance, since it opens the door for decision making and risk management ahead of time.

Decision Making Management +1

Variational Pedestrian Detection

no code implementations CVPR 2021 Yuang Zhang, Huanyu He, Jianguo Li, Yuxi Li, John See, Weiyao Lin

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods.

object-detection Object Detection +2

Extreme Value Preserving Networks

no code implementations17 Nov 2020 MingJie Sun, Jianguo Li, ChangShui Zhang

Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature.

AP-Loss for Accurate One-Stage Object Detection

1 code implementation17 Aug 2020 Kean Chen, Weiyao Lin, Jianguo Li, John See, Ji Wang, Junni Zou

This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem.

Classification General Classification +2

Extreme Values are Accurate and Robust in Deep Networks

no code implementations25 Sep 2019 Jianguo Li, MingJie Sun, ChangShui Zhang

Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature.

ATRW: A Benchmark for Amur Tiger Re-identification in the Wild

1 code implementation13 Jun 2019 Shuyuan Li, Jianguo Li, Hanlin Tang, Rui Qian, Weiyao Lin

This paper tries to fill the gap by introducing a novel large-scale dataset, the Amur Tiger Re-identification in the Wild (ATRW) dataset.

Towards Accurate One-Stage Object Detection with AP-Loss

1 code implementation CVPR 2019 Kean Chen, Jianguo Li, Weiyao Lin, John See, Ji Wang, Ling-Yu Duan, Zhibo Chen, Changwei He, Junni Zou

For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.

Classification General Classification +2

Few Sample Knowledge Distillation for Efficient Network Compression

1 code implementation CVPR 2020 Tianhong Li, Jianguo Li, Zhuang Liu, Chang-Shui Zhang

Deep neural network compression techniques such as pruning and weight tensor decomposition usually require fine-tuning to recover the prediction accuracy when the compression ratio is high.

Knowledge Distillation Network Pruning +2

Composite Binary Decomposition Networks

no code implementations16 Nov 2018 You Qiaoben, Zheng Wang, Jianguo Li, Yinpeng Dong, Yu-Gang Jiang, Jun Zhu

Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precision counterparts.

General Classification Image Classification +3

Knowledge Distillation from Few Samples

no code implementations27 Sep 2018 Tianhong Li, Jianguo Li, Zhuang Liu, ChangShui Zhang

Taking the assumption that both "teacher" and "student" have the same feature map sizes at each corresponding block, we add a $1\times 1$ conv-layer at the end of each block in the student-net, and align the block-level outputs between "teacher" and "student" by estimating the parameters of the added layer with limited samples.

Knowledge Distillation

Object Detection from Scratch with Deep Supervision

1 code implementation25 Sep 2018 Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue

Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method.

General Classification object-detection +1

Network Decoupling: From Regular to Depthwise Separable Convolutions

1 code implementation16 Aug 2018 Jianbo Guo, Yuxi Li, Weiyao Lin, Yurong Chen, Jianguo Li

Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available.

object-detection Object Detection

Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

1 code implementation29 Jul 2018 Yuxi Li, Jiuwei Li, Weiyao Lin, Jianguo Li

Based on the deeply supervised object detection (DSOD) framework, we propose Tiny-DSOD dedicating to resource-restricted usages.

object-detection Object Detection

Boosting Adversarial Attacks with Momentum

6 code implementations CVPR 2018 Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li

To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks.

Adversarial Attack

BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera

no code implementations ICCV 2017 Tao Yu, Kaiwen Guo, Feng Xu, Yuan Dong, Zhaoqi Su, Jianhui Zhao, Jianguo Li, Qionghai Dai, Yebin Liu

To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method.

Surface Reconstruction

Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization

1 code implementation3 Aug 2017 Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, Hang Su

This procedure can greatly compensate the quantization error and thus yield better accuracy for low-bit DNNs.


DSOD: Learning Deeply Supervised Object Detectors from Scratch

4 code implementations ICCV 2017 Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue

State-of-the-art object objectors rely heavily on the off-the-shelf networks pre-trained on large-scale classification datasets like ImageNet, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks.

General Classification object-detection +1

Weakly Supervised Dense Video Captioning

no code implementations CVPR 2017 Zhiqiang Shen, Jianguo Li, Zhou Su, Minjun Li, Yurong Chen, Yu-Gang Jiang, xiangyang xue

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences.

Dense Video Captioning Language Modelling +1

Deep Attributes from Context-Aware Regional Neural Codes

no code implementations8 Sep 2015 Jianwei Luo, Jianguo Li, Jun Wang, Zhiguo Jiang, Yurong Chen

Results show that deep attribute approaches achieve state-of-the-art results, and outperforms existing peer methods with a significant margin, even though some benchmarks have little overlap of concepts with the pre-trained CNN models.

General Classification Image Classification +1

Large-scale Supervised Hierarchical Feature Learning for Face Recognition

no code implementations6 Jul 2014 Jianguo Li, Yurong Chen

Second, the face image is further represented by patches of picked channels, and we search from the over-complete patch pool to activate only those most discriminant patches.

Face Recognition

Learning SURF Cascade for Fast and Accurate Object Detection

no code implementations CVPR 2013 Jianguo Li, Yimin Zhang

Third, we adopt AUC as a single criterion for the convergence test during cascade training rather than the two trade-off criteria (false-positive-rate and hit-rate) in the VJ framework.

object-detection Object Detection

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