Search Results for author: Peng Wu

Found 43 papers, 14 papers with code

You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement

1 code implementation8 Feb 2024 Yixu Feng, Cheng Zhang, Pei Wang, Peng Wu, Qingsen Yan, Yanning Zhang

Further, we design a novel Color and Intensity Decoupling Network (CIDNet) with two branches dedicated to processing the decoupled image brightness and color in the HVI space.

Low-light Image Deblurring and Enhancement Low-Light Image Enhancement

MixMobileNet: A Mixed Mobile Network for Edge Vision Applications

1 code implementation Electronics 2024 Yanju Meng, Peng Wu, Jian Feng, XiaoMing Zhang

For global, we propose the global-feature aggregation encoder (GFAE), which employs a pooling strategy and computes the covariance matrix between channels instead of the spatial dimensions, changing the computational complexity from quadratic to linear, and this accelerates the inference of the model.

Image Classification Inductive Bias +2

Open-Vocabulary Video Anomaly Detection

no code implementations13 Nov 2023 Peng Wu, Xuerong Zhou, Guansong Pang, Yujia Sun, Jing Liu, Peng Wang, Yanning Zhang

Particularly, we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task, and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task.

Anomaly Detection Video Anomaly Detection

A Survey of Methods for Handling Disk Data Imbalance

no code implementations13 Oct 2023 Shuangshuang Yuan, Peng Wu, Yuehui Chen, Qiang Li

Class imbalance exists in many classification problems, and since the data is designed for accuracy, imbalance in data classes can lead to classification challenges with a few classes having higher misclassification costs.

Classification

An improved CTGAN for data processing method of imbalanced disk failure

no code implementations10 Oct 2023 Jingbo Jia, Peng Wu, Hussain Dawood

To address the problem of insufficient failure data generated by disks and the imbalance between the number of normal and failure data.

Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN

no code implementations10 Oct 2023 Shuangshuang Yuan, Peng Wu, Yuehui Chen

Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify in a classification task.

Classification

Human-centric Behavior Description in Videos: New Benchmark and Model

no code implementations4 Oct 2023 Lingru Zhou, Yiqi Gao, Manqing Zhang, Peng Wu, Peng Wang, Yanning Zhang

To address this challenge, we construct a human-centric video surveillance captioning dataset, which provides detailed descriptions of the dynamic behaviors of 7, 820 individuals.

Video Captioning

VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

1 code implementation22 Aug 2023 Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, Yanning Zhang

With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task.

Anomaly Detection Binary Classification +1

Robust Interference Mitigation techniques for Direct Position Estimation

no code implementations9 Aug 2023 Haoqing Li, Shuo Tang, Peng Wu, Pau Closas

Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required.

Position

Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

no code implementations24 Jul 2023 Peng Wu, Jing Liu, Xiangteng He, Yuxin Peng, Peng Wang, Yanning Zhang

In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e. g., language descriptions and synchronous audios.

Anomaly Detection Retrieval +2

Jammer classification with Federated Learning

no code implementations5 Jun 2023 Peng Wu, Helena Calatrava, Tales Imbiriba, Pau Closas

Jamming signals can jeopardize the operation of GNSS receivers until denying its operation.

Classification Federated Learning +1

RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search

1 code implementation18 May 2023 Xuran Li, Peng Wu, Kaixiang Dong, Zhen Zhang, Yanting Chen

This matrix categorizes predictions as true fair, true biased, false fair, and false biased, and the perturbations guided by it can produce a dual impact on instances and their similar counterparts to either undermine prediction accuracy (robustness) or cause biased predictions (individual fairness).

Data Augmentation Fairness

Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations

no code implementations17 Apr 2023 Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu

Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance.

Imputation Recommendation Systems

Bayesian data fusion with shared priors

no code implementations14 Dec 2022 Peng Wu, Tales Imbiriba, Victor Elvira, Pau Closas

When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential.

Bayesian Inference Federated Learning

ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection

no code implementations17 Nov 2022 Yiyang Shen, Rongwei Yu, Peng Wu, Haoran Xie, Lina Gong, Jing Qin, Mingqiang Wei

We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds.

3D Object Detection object-detection

A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction

no code implementations12 Nov 2022 Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Jie Sun

However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.

Generalization Bounds Imputation +1

PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection

no code implementations29 Aug 2022 Peng Wu, Lipeng Gu, Xuefeng Yan, Haoran Xie, Fu Lee Wang, Gary Cheng, Mingqiang Wei

Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.

3D Object Detection Novel Object Detection +3

Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection

1 code implementation22 Jul 2022 Zhiwei Yang, Peng Wu, Jing Liu, Xiaotao Liu

Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space.

Anomaly Detection

Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting

no code implementations12 Jul 2022 Hao liu, Bin Chen, Bo wang, Chunpeng Wu, Feng Dai, Peng Wu

To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2.

object-detection Object Detection +1

Multiple Robust Learning for Recommendation

no code implementations9 Jul 2022 Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Xiao-Hua Zhou, Peng Wu

Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.

Imputation Recommendation Systems

Unsupervised High-Resolution Portrait Gaze Correction and Animation

1 code implementation1 Jul 2022 Jichao Zhang, Jingjing Chen, Hao Tang, Enver Sangineto, Peng Wu, Yan Yan, Nicu Sebe, Wei Wang

Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels.

Image Inpainting Vocal Bursts Intensity Prediction

Accurate Fairness: Improving Individual Fairness without Trading Accuracy

1 code implementation18 May 2022 Xuran Li, Peng Wu, Jing Su

We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy.

BIG-bench Machine Learning Fairness

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random

no code implementations10 May 2022 Haoxuan Li, Chunyuan Zheng, Peng Wu

However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities.

Generalization Bounds Imputation +1

MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

4 code implementations2 May 2022 Minghui Yang, Peng Wu, Jing Liu, Hui Feng

By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner.

Anomaly Detection Defect Detection +1

TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations

no code implementations19 Mar 2022 Haoxuan Li, Yan Lyu, Chunyuan Zheng, Peng Wu

Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning.

Imputation Recommendation Systems +1

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

1 code implementation23 Feb 2022 Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, yuhao deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou

To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.

Causal Inference Descriptive +2

From Rough to Multifractal volatility: the log S-fBM model

no code implementations24 Jan 2022 Peng Wu, Jean-François Muzy, Emmanuel Bacry

We introduce a family of random measures $M_{H, T} (d t)$, namely log S-fBM, such that, for $H>0$, $M_{H, T}(d t) = e^{\omega_{H, T}(t)} d t$ where $\omega_{H, T}(t)$ is a Gaussian process that can be considered as a stationary version of an $H$-fractional Brownian motion.

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges

no code implementations18 Jan 2022 Peng Wu, Haoxuan Li, yuhao deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou

Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.

Causal Inference Recommendation Systems

An Intelligent Energy Management Framework for Hybrid-Electric Propulsion Systems Using Deep Reinforcement Learning

no code implementations31 Jul 2021 Peng Wu, Julius Partridge, Enrico Anderlini, Yuanchang Liu, Richard Bucknall

In the proposed framework, a Twin-Delayed Deep Deterministic Policy Gradient agent is trained using an extensive volume of historical load profiles to generate a generic energy management strategy.

energy management Management +1

HANet: Hierarchical Alignment Networks for Video-Text Retrieval

1 code implementation26 Jul 2021 Peng Wu, Xiangteng He, Mingqian Tang, Yiliang Lv, Jing Liu

Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text.

Retrieval Text Matching +3

Personalized Federated Learning over non-IID Data for Indoor Localization

no code implementations9 Jul 2021 Peng Wu, Tales Imbiriba, Junha Park, Sunwoo Kim, Pau Closas

Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models.

Indoor Localization Personalized Federated Learning

GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking

no code implementations Findings of the Association for Computational Linguistics 2020 Peng Wu, Bowei Zou, Ridong Jiang, AiTi Aw

As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems.

Dialogue State Tracking Multi-domain Dialogue State Tracking +1

Time Difference of Arrival (TDoA) Localization Combining Weighted Least Squares and Firefly Algorithm

no code implementations MDPI 2019 Peng Wu, Shaojing Su, Zhen Zuo *, Xiaojun Guo, Bei Sun and Xudong Wen

This paper proposes a hybrid firefly algorithm (hybrid‐FA) method, combining the weighted least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high accuracy.

Multimodal Deep Network Embedding with Integrated Structure and Attribute Information

no code implementations28 Mar 2019 Conghui Zheng, Li Pan, Peng Wu

Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features.

Attribute Network Embedding

Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations

no code implementations11 Oct 2018 Lan Hu, Yuchen Cao, Peng Wu, Laurent Kneip

Most problems involving simultaneous localization and mapping can nowadays be solved using one of two fundamentally different approaches.

Object Reconstruction Simultaneous Localization and Mapping +1

Localization length exponent in two models of quantum Hall plateau transitions

1 code implementation2 Apr 2018 Qiong Zhu, Peng Wu, R. N. Bhatt, Xin Wan

Motivated by the recent numerical studies on the Chalker-Coddington network model that found a larger-than-expected critical exponent of the localization length characterizing the integer quantum Hall plateau transitions, we revisited the exponent calculation in the continuum model and in the lattice model, both projected to the lowest Landau level or subband.

Disordered Systems and Neural Networks

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