Author name cluster

Xiaoliang Wang

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8 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Riding the Wave: Multi-Scale Spatial-Temporal Graph Learning for Highway Traffic Flow Prediction Under Overload Scenarios

Xigang Sun
Jiahui Jin
Hancheng Wang
Xiangguo Sun
Xiaoliang Wang
Jun Zhu

Highway traffic flow prediction under overload scenarios (HIPO) is a critical problem in intelligent transportation systems, which aims to forecast future traffic patterns on highway segments during periods of exceptionally high demand. Despite its importance, this problem has rarely been explored in recent research due to the unique challenges posed by irregular flow patterns, complex traffic behaviors, and sparse contextual data. In this paper, we propose a Heterogeneous Spatial-Temporal graph network With Adaptive contrastiVE learning (HST-WAVE) to address the HIPO problem. Specifically, we first construct a heterogeneous traffic graph according to the physical highway structure. Then, we develop a multi-scale temporal weaving Transformer and a coupled heterogeneous graph attention network to capture the irregular traffic flow patterns and complex transition behaviors. Furthermore, we introduce an adaptive temporal enhancement contrastive learning strategy to bridge the gap between divergent temporal patterns and mitigate data sparsity. We conduct extensive experiments on two real-world highway network datasets (No. G56 and G60 in Hangzhou, China), showing that our model can effectively handle the HIPO problem and achieve state-of-the-art performance. The source code is available at https: //github. com/luck-seu/HST-WAVE.

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NeurIPS Conference 2025 Conference Paper

SmallKV: Small Model Assisted Compensation of KV Cache Compression for Efficient LLM Inference

Yi Zhao
Yajuan Peng
Nguyen Cam-Tu
Zuchao Li
Xiaoliang Wang
Hai Zhao
Xiaoming Fu

KV cache eviction has emerged as an effective solution to alleviate resource constraints faced by LLMs in long-context scenarios. However, existing token-level eviction methods often overlook two critical aspects: (1) their irreversible eviction strategy fails to adapt to dynamic attention patterns during decoding (the saliency shift problem), and (2) they treat both marginally important tokens and truly unimportant tokens uniformly, despite the collective significance of marginal tokens to model performance (the marginal information over-compression problem). To address these issues, we design two compensation mechanisms based on the high similarity of attention matrices between LLMs with different scales. We propose SmallKV, a small model assisted compensation method for KV cache compression. SmallKV can maintain attention matching between different-scale LLMs to: 1) assist the larger model in perceiving globally important information of attention; and 2) use the smaller model’s attention scores to approximate those of marginal tokens in the larger model. Extensive experiments on benchmarks including GSM8K, BBH, MT-Bench, and LongBench demonstrate the effectiveness of SmallKV. Moreover, efficiency evaluations show that SmallKV achieves 1. 75 - 2. 56 times higher throughput than baseline methods, highlighting its potential for efficient and performant LLM inference in resource constrained environments.

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ICRA Conference 2021 Conference Paper

PSF-LO: Parameterized Semantic Features Based Lidar Odometry

Guibin Chen
Bosheng Wang
Xiaoliang Wang
Huanjun Deng
Bing Wang
Shuo Zhang

Lidar odometry (LO) is a key technology in numerous reliable and accurate localization and mapping systems of autonomous driving. The state-of-the-art LO methods generally leverage geometric information to perform point cloud registration. Furthermore, obtaining the point cloud semantic information describing the environment more abundantly will facilitate the registration. We present a novel semantic lidar odometry method based on self-designed parameterized seman-tic features (PSFs) to achieve low-drift ego-motion estimation for autonomous vehicle in real time. We first use a convolutional neural network-based algorithm to obtain point-wise semantics from the input laser point cloud, and then use semantic labels to separate road, building, traffic sign and pole-like point cloud and fit them separately to obtain corresponding PSFs. A fast PSF-based matching enables us to refine geometric features (GeFs) registration, thereby reducing the impact of blurred submap surface on the accuracy of GeFs matching. Besides, we design an efficient instance-level method to accurately recognize and remove the dynamic objects while retaining static ones in the semantic point cloud, which are beneficial to further improve the accuracy of LO. We evaluate our method, namely PSF-LO, on the public dataset KITTI Odometry Benchmark and rank #1 among semantic lidar methods with an average translational error of 0. 82% in the test dataset.

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IROS Conference 2018 Conference Paper

Design and Implementation of a Novel Aerial Manipulator with Tandem Ducted Fans

Yibo Zhang
Changle Xiang
Bin Xu
Yang Wang
Xiaoliang Wang

This paper proposes a novel aerial manipulator with tandem ducted fans, which takes both trafficability and effective loading into account. The aerial manipulator is particularly suitable for grasping in complex and narrow environment, in which traditional multi-rotor and helicopter would be inaccessible. The comprehensive integrated dynamic model is established by taking the aerial vehicle dynamics and manipulator dynamics as a whole. On this basis, a multilayer composite controller with feedforward compensation is designed, considering the mutual reactive influence between the aerial vehicle and the manipulator to improve the stability of the system under the motion of the manipulator. The simulation and actual flight tests verify the effectiveness of the design and show good stability and tracking performance of the system.

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IJCAI Conference 2017 Conference Paper

AGRA: An Analysis-Generation-Ranking Framework for Automatic Abbreviation from Paper Titles

Jianbing Zhang
Yixin Sun
Shujian Huang
Cam-Tu Nguyen
Xiaoliang Wang
Xinyu Dai
Jiajun Chen
Yang Yu

People sometimes choose word-like abbreviations to refer to items with a long description. These abbreviations usually come from the descriptive text of the item and are easy to remember and pronounce, while preserving the key idea of the item. Coming up with a nice abbreviation is not an easy job, even for human. Previous assistant naming systems compose names by applying hand-written rules, which may not perform well. In this paper, we propose to view the naming task as an artificial intelligence problem and create a data set in the domain of academic naming. To generate more delicate names, we propose a three-step framework, including description analysis, candidate generation and abbreviation ranking, each of which is parameterized and optimizable. We conduct experiments to compare different settings of our framework with several analysis approaches from different perspectives. Compared to online or baseline systems, our framework could achieve the best results.

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ICRA Conference 2017 Conference Paper

Real-time visual tracking via robust Kernelized Correlation Filter

Xiaoliang Wang
Marie O'Brien
Changle Xiang
Bin Xu
Homayoun Najjaran

There has been an increasing interest in the use of correlation filters for visual object tracking due to their impressive tracking performance. However, existing correlation filter based tracking methods, such as Struck and Kernelized Correlation Filter (KCF), cannot always solve tracking problems in complicated conditions such as heavy occlusion and aggressive motion. In this paper, we proposed a real-time visual tracker via a robust KCF. We start by implementing a search window alignment, based on a motion model with uncertainty, which increases the tracking accuracy for fast moving targets and reduces the padding value to accelerate tracking speed. Next, we establish a combined confidence measurement including occlusion information, which is utilized for robust updating. Then we apply an adaptive Kalman filter to improve the tracking accuracy. Qualitative and quantitative experimental results show that the proposed algorithm outperforms the state-of-the-art methods such as KCF and Struck.

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JBHI Journal 2014 Journal Article

Enabling Smart Personalized Healthcare: A Hybrid Mobile-Cloud Approach for ECG Telemonitoring

Xiaoliang Wang
Qiong Gui
Bingwei Liu
Zhanpeng Jin
Yu Chen

The severe challenges of the skyrocketing healthcare expenditure and the fast aging population highlight the needs for innovative solutions supporting more accurate, affordable, flexible, and personalized medical diagnosis and treatment. Recent advances of mobile technologies have made mobile devices a promising tool to manage patients' own health status through services like telemedicine. However, the inherent limitations of mobile devices make them less effective in computation- or data-intensive tasks such as medical monitoring. In this study, we propose a new hybrid mobile-cloud computational solution to enable more effective personalized medical monitoring. To demonstrate the efficacy and efficiency of the proposed approach, we present a case study of mobile-cloud based electrocardiograph monitoring and analysis and develop a mobile-cloud prototype. The experimental results show that the proposed approach can significantly enhance the conventional mobile-based medical monitoring in terms of diagnostic accuracy, execution efficiency, and energy efficiency, and holds the potential in addressing future large-scale data analysis in personalized healthcare.

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AAAI Conference 2014 Conference Paper

Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning

Cam-Tu Nguyen
Xiaoliang Wang
Jing Liu
Zhi-Hua Zhou

Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference. The model can naturally deal with examples in which some views could be absent (partial examples). On multi-view datasets, it is shown that our method is better than other multi-view and single-view approaches particularly in the presence of partial examples. On single-view benchmarks, extensive evaluation shows that our method is highly competitive or better than other MIML approaches on labeling examples and instances. Moreover, our method can effectively handle datasets with a large number of labels.

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