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Bin Zhou

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14 papers

2 author rows

AAAI Conference 2026 Conference Paper

Schema-Guided Event Reasoning: A Plug-and-Play Event Reasoning Framework Based on Large Language Models

Yuying Liu
Xuechen Zhao
Yanyi Huang
Ye Wang
Xin Song
Yue Zhang
Haiyan Liu
Bin Zhou

Recent advancements in Large Language Models have increasingly demonstrated their potential for event reasoning. However, LLMs still struggle with this task due to inadequate modeling of event structures. Although introducing schema knowledge has been shown to improve event reasoning performance, existing methods rely on predefined schema library, compromising their scalability and lightweight deployment. To address these challenges, we propose SGER, a plug-and-play Schema-Guided Event Reasoning framework. In the schema extraction stage, the model maps event descriptions with diverse surface forms to potential semantic structure representations, achieving an abstract transformation from instances to schemas. The schema prediction stage captures the potential associations between historical event schemas to make forward-looking inferences about possible future event schemas. In the event reasoning stage, we integrate historical events and predicted schemas into prompts to guide LLMs in generating specific, contextually consistent predicted events. Experimental evaluations demonstrate that our framework significantly improves event reasoning performance of LLMs.

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EAAI Journal 2025 Journal Article

Enhanced targeted attacks on Graph Neural Networks via Average Gradient and Perturbation Optimization

Yang Chen
Bin Zhou
Haixing Zhao
Padarti Vijaya Kumar

Graph Neural Networks (GNNs) are vulnerable to adversarial attacks that cause performance degradation by adding small perturbations to the graph. Gradient-based attacks are among the most widely used methods and have demonstrated strong performance across various attack scenarios. However, most gradient attacks use greedy strategies to generate perturbations, which tend to fall into local optima, leading to underperformance of the attack. To address the above problem, we propose an attack (Average Gradient and Perturbation Optimization Attack, AGPOA) on GNNs, which consists of an average gradient calculation and a perturbation optimization module. In the average gradient calculation module, we compute the average of the gradient information over all moments to guide the attack to generate perturbed edges, which stabilizes the direction of the attack update and gets rid of undesirable local maxima. We use a perturbation optimization module to limit the attack budget and further improve performance. Furthermore, we demonstrate the theoretical superiority of AGPOA over traditional gradient-based attack methods through attack loss variance. The experimental results show that AGPOA improves the misclassification rate by 2%–8% compared to other state-of-the-art models in the node classification task.

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ICRA Conference 2025 Conference Paper

HGAT-CP: Heterogeneous Graph Attention Network for Collision Prediction in Autonomous Driving

Yongzhi Jiang
Bin Zhou
Yongwei Li
Xinkai Wu
Zhongxia Xiong

Predicting potential collision events is beneficial to ensure the driving safety of autonomous vehicles. Existing graph-based collision prediction methods rely heavily on domain knowledge and predefined semantic relations, limiting their flexibility and adaptability in complex driving scenarios. To overcome these challenges, this paper introduces a novel collision prediction framework named HGAT-CP, which integrates a Heterogeneous Graph Attention Network (HGAT) with a Long Short-Term Memory network (LSTM) to model the spatial-temporal interactions in scenes. First, the proposed method employs a data-driven scene graph embedding module to autonomously learn relationships between vehicles and lanes and construct flexible scene graphs. Then, the HGAT module utilizes a dual-level attention mechanism, operating at both the node level and type level, to capture spatial interactions without relying on predefined semantic rules. The LSTM module models temporal dependencies of the scene graph embeddings to improve the prediction of collision events over time. Experimental evaluations on public datasets demonstrate that our proposed method achieves state-of-the-art performance, outperforming existing methods across all metrics.

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AAAI Conference 2025 Conference Paper

LLM-DR: A Novel LLM-Aided Diffusion Model for Rule Generation on Temporal Knowledge Graphs

Kai Chen
Xin Song
Ye Wang
Liqun Gao
Aiping Li
Xiaojuan Zhao
Bin Zhou
Yalong Xie

Among various temporal knowledge graph (TKG) extrapolation methods, rule-based approaches stand out for their explicit rules and transparent reasoning paths. However, the vast search space for rule extraction poses a challenge in identifying high-quality logic rules. To navigate this challenge, we explore the use of generation models to generate new rules, thereby enriching our rule base and enhancing our reasoning capabilities. In this paper, we introduce LLM-DR, an innovative rule-based method for TKG extrapolation, which harnesses diffusion models to generate rules that are consistent with the distribution of the source data, while also amalgamating the rich semantic insights of Large Language Models (LLMs). Specifically, our LLM-DR generates semantically relevant and high-quality rules, employing conditional diffusion models in a classifier-free guidance fashion and refining them with LLM-based constraints. To assess rule efficacy, we meticulously design a coarse-to-fine evaluation strategy that initiates with coarse-grained filtering to eliminate less plausible rules and proceeds with fine-grained scoring to quantify the reliability of the retained. Extensive experiments demonstrate the promising capacity of our LLM-DR.

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AAAI Conference 2025 Conference Paper

Social Recommendation via Graph-Level Counterfactual Augmentation

Yinxuan Huang
Ke Liang
Yanyi Huang
Xiang Zeng
Kai Chen
Bin Zhou

Traditional recommendation system focus more on the correlations between users and items (user-item relationships), while research on user-user relationships has received significant attention these years, which is also known as social recommendation. Graph-based models have achieved a great success in this task by utilizing the complex topological information of the social networks. However, these models still face the insufficient expressive and overfitting problems. Counterfactual approaches are proven effective as information augmentation strategies towards above issues in various scenarios, but not fully utilized in social recommendations. To this end, we propose a novel social recommendation method, termed SR-GCA, via a plug-and-play Graph-Level Counterfactual Augmentation mechanism. Specifically, we first generate counterfactual social and item links by constructing a counterfactual matrix for data aug- mentation. Then, we employ a supervised learning strategy to refine data both factual and counterfactual links. Thirdly, we enhance representations learning between users via an alignment and self-supervised optimization techniques. Extensive experiments demonstrate the promising capacity of our model from five aspects, including superiority, effectively, transfer- ability, complexity, sensitively. In particular, the transferability is well-proven by extending our GCA module to three typical social recommendation models.

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YNIMG Journal 2024 Journal Article

Olfaction modulates cortical arousal independent of perceived odor intensity and pleasantness

Fangshu Yao
Xiaoyue Chang
Bin Zhou
Wen Zhou

Throughout history, various odors have been harnessed to invigorate or relax the mind. The mechanisms underlying odors' diverse arousal effects remain poorly understood. We conducted five experiments (184 participants) to investigate this issue, using pupillometry, electroencephalography, and the attentional blink paradigm, which exemplifies the limit in attentional capacity. Results demonstrated that exposure to citral, compared to vanillin, enlarged pupil size, reduced resting-state alpha oscillations and alpha network efficiency, augmented beta-gamma oscillations, and enhanced the coordination between parietal alpha and frontal beta-gamma activities. In parallel, it attenuated the attentional blink effect. These effects were observed despite citral and vanillin being comparable in perceived odor intensity, pleasantness, and nasal pungency, and were unlikely driven by semantic biases. Our findings reveal that odors differentially alter the small-worldness of brain network architecture, and thereby brain state and arousal. Furthermore, they establish arousal as a unique dimension in olfactory space, distinct from intensity and pleasantness.

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IJCAI Conference 2023 Conference Paper

CADParser: A Learning Approach of Sequence Modeling for B-Rep CAD

Shengdi Zhou
Tianyi Tang
Bin Zhou

Computer-Aided Design (CAD) plays a crucial role in industrial manufacturing by providing geometry information and the construction workflow for manufactured objects. The construction information enables effective re-editing of parametric CAD models. While boundary representation (B-Rep) is the standard format for representing geometry structures, JSON format is an alternative due to the lack of uniform criteria for storing the construction workflow. Regrettably, most CAD models available on the Internet only offer geometry information, omitting the construction procedure and hampering creation efficiency. This paper proposes a learning approach CADParser to infer the underlying modeling sequences given a B-Rep CAD model. It achieves this by treating the CAD geometry structure as a graph and the construction workflow as a sequence. Since the existing CAD dataset only contains two operations (i. e. , Sketch and Extrusion), limiting the diversity of the CAD model creation, we also introduce a large-scale dataset incorporating a more comprehensive range of operations such as Revolution, Fillet, and Chamfer. Each model includes both the geometry structure and the construction sequences. Extensive experiments demonstrate that our method can compete with the existing state-of-the-art methods quantitatively and qualitatively. Data is available at https: //drive. google. com/CADParserData.

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ICRA Conference 2023 Conference Paper

Fast Event-based Double Integral for Real-time Robotics

Shijie Lin
Yingqiang Zhang
Dongyue Huang
Bin Zhou
Xiaowei Luo
Jia Pan 0001

Motion deblurring is a critical ill-posed problem that is important in many vision-based robotics applications. The recently proposed event-based double integral (EDI) provides a theoretical framework for solving the deblurring prob-lem with the event camera and generating clear images at high frame-rate. However, the original EDI is mainly designed for offline computation and does not support real-time requirement in many robotics applications. In this paper, we propose the fast EDI, an efficient implementation of EDI that can achieve real-time online computation on single-core CPU devices, which is common for physical robotic platforms used in practice. In experiments, our method can handle event rates at as high as 13 million event per second in a wide variety of challenging lighting conditions. We demonstrate the benefit on multiple downstream real-time applications, including localization, vi-sual tag detection, and feature matching.

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AAAI Conference 2022 Conference Paper

P^3-Net: Part Mobility Parsing from Point Cloud Sequences via Learning Explicit Point Correspondence

Yahao Shi
Xinyu Cao
Feixiang Lu
Bin Zhou

Understanding an articulated 3D object with its movable parts is an essential skill for an intelligent agent. This paper presents a novel approach to parse 3D part mobility from point cloud sequences. The key innovation is learning explicit point correspondence from a raw unordered point cloud sequence. We propose a novel deep network called P3 -Net to parallelize the trajectory feature extraction and the point correspondence establishment, performing joint optimization between them. Specifically, we design a Match-LSTM module to reaggregate point features among different frames by a point correspondence matrix, a. k. a. the matching matrix. To obtain this matrix, an attention module is proposed to calculate the point correspondence. Moreover, we implement a Gumbel-Sinkhorn module to reduce the many-to-one relationship for better point correspondence. We conduct comprehensive evaluations on public benchmarks, including the motion dataset and the PartNet dataset. Results demonstrate that our approach outperforms SOTA methods on various 3D parsing tasks of part mobility, including motion flow prediction, motion part segmentation, and motion attribute (i. e. , axis & range) estimation. Moreover, we integrate our approach into a robot perception module to validate its robustness.

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NeurIPS Conference 2020 Conference Paper

Compositional Generalization by Learning Analytical Expressions

Qian Liu
Shengnan An
Jian-Guang Lou
Bei Chen
Zeqi Lin
Yan Gao
Bin Zhou
Nanning Zheng

Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily. However, existing neural network based models have been proven to be extremely deficient in such a capability. Inspired by work in cognition which argues compositionality can be captured by variable slots with symbolic functions, we present a refreshing view that connects a memory-augmented neural model with analytical expressions, to achieve compositional generalization. Our model consists of two cooperative neural modules, Composer and Solver, fitting well with the cognitive argument while being able to be trained in an end-to-end manner via a hierarchical reinforcement learning algorithm. Experiments on the well-known benchmark SCAN demonstrate that our model seizes a great ability of compositional generalization, solving all challenges addressed by previous works with 100% accuracies.

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IJCAI Conference 2020 Conference Paper

How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context

Qian Liu
Bei Chen
Jiaqi Guo
Jian-Guang Lou
Bin Zhou
Dongmei Zhang

Recently semantic parsing in context has received a considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic parsing in context. We present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. We evaluate 13 context modeling methods on two large complex cross-domain datasets, and our best model achieves state-of-the-art performances on both datasets with significant improvements. Furthermore, we summarize the most frequent contextual phenomena, with a fine-grained analysis on representative models, which may shed light on potential research directions. Our code is available at https: //github. com/microsoft/ContextualSP.

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NeurIPS Conference 2020 Conference Paper

PIE-NET: Parametric Inference of Point Cloud Edges

Xiaogang Wang
Yuelang Xu
Kai Xu
Andrea Tagliasacchi
Bin Zhou
Ali Mahdavi-Amiri
Hao Zhang

We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data. We represent these edges as a collection of parametric curves (i. e. ,~lines, circles, and B-splines). Accordingly, our deep neural network, coined PIE-NET, is trained for parametric inference of edges. The network relies on a "region proposal" architecture, where a first module proposes an over-complete collection of edge and corner points, and a second module ranks each proposal to decide whether it should be considered. We train and evaluate our method on the ABC dataset, a large dataset of CAD models, and compare our results to those produced by traditional (non-learning) processing pipelines, as well as a recent deep learning based edge detector (EC-NET). Our results significantly improve over the state-of-the-art from both a quantitative and qualitative standpoint.

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ICRA Conference 2020 Conference Paper

Real-Time UAV Path Planning for Autonomous Urban Scene Reconstruction

Qi Kuang
Jinbo Wu
Jia Pan 0001
Bin Zhou

Unmanned aerial vehicles (UAVs) are frequently used for large-scale scene mapping and reconstruction. However, in most cases, drones are operated manually, which should be more effective and intelligent. In this article, we present a method of real-time UAV path planning for autonomous urban scene reconstruction. Considering the obstacles and time costs, we utilize the top view to generate the initial path. Then we estimate the building heights and take close-up pictures that reveal building details through a SLAM framework. To predict the coverage of the scene, we propose a novel method which combines information on reconstructed point clouds and possible coverage areas. The experimental results reveal that the reconstruction quality of our method is good enough. Our method is also more time-saving than the state-of-the-arts.

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IJCAI Conference 2019 Conference Paper

BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series

Bin Zhou
Shenghua Liu
Bryan Hooi
Xueqi Cheng
Jing Ye

Given a large-scale rhythmic time series containing mostly normal data segments (or `beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For example, how can we detect anomalous beats from electrocardiogram (ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs explainable results to pinpoint the anomalous time ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error using an adversarial generation approach, as well as data augmentation using time series warping. Experiments show that BeatGAN accurately and efficiently detects anomalous beats in ECG time series, and routes doctors' attention to anomalous time ticks, achieving accuracy of nearly 0. 95 AUC, and very fast inference (2. 6 ms per beat). In addition, we show that BeatGAN accurately detects unusual motions from multivariate motion-capture time series data, illustrating its generality.

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