Jingjing Wang

AAAI Conference 2026 Conference Paper

Towards Closed-Loop Embodied Empathy Evolution: Probing LLM-Centric Lifelong Empathic Motion Generation in Unseen Scenarios

• Jiawen Wang
• Jingjing Wang
• Tianyang Chen
• Min Zhang
• Guodong Zhou

In the literature, existing human-centric emotional motion generation methods primarily focus on boosting performance within a single scale-fixed dataset, largely neglecting the flexible and scale-increasing motion scenarios (e.g., sports, dance), whereas effectively learning these newly emerging scenarios can significantly enhance the model’s real-world generalization ability. Inspired by this, this paper proposes a new LLM-Centric Lifelong Empathic Motion Generation (L2-EMG) task, which aims to equip LLMs with the capability to continually acquire emotional motion generation knowledge across different unseen scenarios, potentially contributing to building a closed-loop and self-evolving embodied agent equipped with both empathy and intelligence. Further, this paper poses two key challenges in the L2-EMG task, i.e., the emotion decoupling challenge and the scenario adapting challenge. To this end, this paper proposes an Emotion-Transferable and Scenario-Adapted Mixture of Experts (ES-MoE) approach which designs a causal-guided emotion decoupling block and a scenario-adapted expert constructing block to address the two challenges, respectively. Especially, this paper constructs multiple L2-EMG datasets to validate the effectiveness of the ES-MoE approach. Extensive evaluations show that ES-MoE outperforms advanced baselines.

ICRA Conference 2025 Conference Paper

Compliance Control with Dynamic and Self-Sensing Hydraulic Artificial Muscles for Wearable Assistive Devices

• Bibhu Sharma
• Emanuele Nicotra
• James Davies 0002
• Chi Cong Nguyen
• Phuoc Thien Phan
• Adrienne Ji
• Kefan Zhu
• Jingjing Wang

While wearable robots that utilize intrinsically soft materials for actuation offer enhanced safety and biological compatibility, the challenges of sensing and control significantly affect their performance. The control problem in such systems is inherently complex, and the inclusion of 'softness' introduces additional nonlinearities, hysteresis, and uncertainties. Furthermore, the effectiveness of control strategies is highly dependent on sensor selection and integration, which presents its own challenges. Most robotic systems require separate sensors for control purposes. In this study, a new sensing and control scheme are introduced for soft wearable robots, leveraging the intrinsic soft-sensing capability of fluidic filament actuators without adding computational complexity. This method enables simultaneous sensing and actuation with $\mathbf{9 6 \%}$ position accuracy, even under physical disturbances. This approach is demonstrated with a soft assistive device for elbow flexion/extension, achieving 70. 5% tracking accuracy and a 0. 09s response delay to human intention, ensuring the system provides minimal resistance when assistance is not needed, while delivering the required support when necessary.

IJCAI Conference 2025 Conference Paper

Denoising Diffusion Models are Good General Gaze Feature Learners

• Guanzhong Zeng
• Jingjing Wang
• Pengwei Yin
• Zefu Xu
• Mingyang Zhou

Since the collection of labeled gaze data is laborious and time-consuming, methods which can learn generalizable features by leveraging large-scale available unlabeled data are desirable. In recent years, we have witnessed the tremendous capabilities of diffusion models in generating images as well as their potential in feature representation learning. In this paper, we investigate whether they can acquire discriminative representations for gaze estimation via generative pre-training. To achieve this goal, we propose a self-supervised learning framework with diffusion models for gaze estimation, called GazeDiff. Specifically, we utilize a conditional diffusion model to generate target image with gaze direction specified by the reference image as the pre-training task. To facilitate the diffusion model to learn gaze related features as condition, we propose a disentangling feature learning strategy, which first learns appearance feature, head pose feature, and eye direction feature respectively, and then combines them as the conditional features. Extensive experiments demonstrate denoising diffusion models are also good general gaze feature learners.

AAAI Conference 2025 Conference Paper

Gaze Label Alignment: Alleviating Domain Shift for Gaze Estimation

• Guanzhong Zeng
• Jingjing Wang
• Zefu Xu
• Pengwei Yin
• Wenqi Ren
• Di Xie
• Jiang Zhu

Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation of data distribution, however, they ignore the existence of label deviation in the data due to the acquisition mechanism of the gaze label and the individual physiological differences. In this paper, we first point out that the influence brought by the label deviation cannot be ignored, and propose a gaze label alignment algorithm (GLA) to eliminate the label distribution deviation. Specifically, we first train the feature extractor on all domains to get domain invariant features, and then select an anchor domain to train the gaze regressor. We predict the gaze label on remaining domains and use a mapping function to align the labels. Finally, these aligned labels can be used to train gaze estimation models. Therefore, our method can be combined with any existing method. Experimental results show that our GLA method can effectively alleviate the label distribution shift, and SOTA gaze estimation methods can be further improved obviously.

IROS Conference 2025 Conference Paper

KD-RIEKF: Kinodynamic Right-Invariant EKF for Legged Robot State Estimation

• Qi Yang
• Bin Lan
• Bingjie Chen
• Jingjing Wang
• Yi Cheng
• Yizhe Li
• Houde Liu
• Bin Liang

We present KD-RIEKF, a novel state estimation framework that incorporates kinodynamic constraints into the Right-Invariant Extended Kalman Filter (RIEKF). Our framework integrates generalized momentum-based contact estimation, centroidal dynamics, and a noise-adaptive module, improving state estimation accuracy by probabilistically adjusting propagation noise to account for contact uncertainty and sensor noise. A key innovation is the expansion of the ground reaction force (GRF) into a state variable. By using GRF-based acceleration as a measurement, our method significantly reduces estimation errors in position, velocity, and orientation. The integration of contact-force-driven adaptive noise effectively boosts the stability of estimation, especially when the system is undergoing turning, acceleration, or deceleration processes. We validated our algorithm in simulation on highly uneven terrain, showing significant enhancements in z-axis position estimation compared to RIEKF. Further experiments on the Unitree Go2 robot across different speeds demonstrated that even in high-speed scenarios over 200 meters, our method reduced position estimation relative error (RE) by 47% and orientation estimation by 42%, confirming its robustness and accuracy under dynamic locomotion.

AAAI Conference 2025 Conference Paper

SQLFixAgent: Towards Semantic-Accurate Text-to-SQL Parsing via Consistency-Enhanced Multi-Agent Collaboration

• Jipeng Cen
• Jiaxin Liu
• Zhixu Li
• Jingjing Wang

While fine-tuned large language models (LLMs) excel in generating grammatically valid SQL in Text-to-SQL parsing, they often struggle to ensure semantic accuracy in queries, leading to user confusion and diminished system usability. To tackle this challenge, we introduce SQLFixAgent, a new consistency-enhanced multi-agent collaborative framework designed for detecting and repairing erroneous SQL. Our framework comprises a core agent, SQLRefiner, alongside two auxiliary agents: SQLReviewer and QueryCrafter. The SQLReviewer agent employs the rubber duck debugging method to identify potential semantic mismatches between SQL and user query. If the error is detected, the QueryCrafter agent generates multiple SQL as candidate repairs using a fine-tuned SQLTool. Subsequently, leveraging similar repair retrieval and failure memory reflection, the SQLRefiner agent selects the most fitting SQL statement from the candidates as the final repair. We evaluated our proposed framework on five Text-to-SQL benchmarks. The experimental results show that our method consistently enhances the performance of the baseline model, specifically achieving an execution accuracy improvement of over 3% on the Bird benchmark. Our framework also has a higher token efficiency compared to other advanced methods, making it more competitive.

EAAI Journal 2025 Journal Article

SSmokeDet: A novel network dedicated to small-scale smoke detection

• Jingjing Wang
• Li Wang
• Runze Zhang
• Xiaochuan Li
• Baoyu Fan

AAAI Conference 2024 Conference Paper

Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning

• Hang Du
• Xuejun Yan
• Jingjing Wang
• Di Xie
• Shiliang Pu

Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications. To achieve this, most previous approaches formulate it as a problem of surface approximation and employ point-based networks to learn surface representations. However, learning surfaces from sparse point clouds is more challenging, and thus they often suffer from the low-fidelity geometry approximation. To address it, we propose an arbitrary-scale Point cloud Upsampling framework using Voxel-based Network (PU-VoxelNet). Thanks to the completeness and regularity inherited from the voxel representation, voxel-based networks are capable of providing predefined grid space to approximate 3D surface, and an arbitrary number of points can be reconstructed according to the predicted density distribution within each grid cell. However, we investigate the inaccurate grid sampling caused by imprecise density predictions. To address this issue, a density-guided grid resampling method is developed to generate high-fidelity points while effectively avoiding sampling outliers. Further, to improve the fine-grained details, we present an auxiliary training supervision to enforce the latent geometric consistency among local surface patches. Extensive experiments indicate the proposed approach outperforms the state-of-the-art approaches not only in terms of fixed upsampling rates but also for arbitrary-scale upsampling. The code is available at https://github.com/hikvision-research/3DVision

AAAI Conference 2024 Conference Paper

CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model

• Pengwei Yin
• Guanzhong Zeng
• Jingjing Wang
• Di Xie

Gaze estimation methods often experience significant performance degradation when evaluated across different domains, due to the domain gap between the testing and training data. Existing methods try to address this issue using various domain generalization approaches, but with little success because of the limited diversity of gaze datasets, such as appearance, wearable, and image quality. To overcome these limitations, we propose a novel framework called CLIP-Gaze that utilizes a pre-trained vision-language model to leverage its transferable knowledge. Our framework is the first to leverage the vision-and-language cross-modality approach for gaze estimation task. Specifically, we extract gaze-relevant feature by pushing it away from gaze-irrelevant features which can be flexibly constructed via language descriptions. To learn more suitable prompts, we propose a personalized context optimization method for text prompt tuning. Furthermore, we utilize the relationship among gaze samples to refine the distribution of gaze-relevant features, thereby improving the generalization capability of the gaze estimation model. Extensive experiments demonstrate the excellent performance of CLIP-Gaze over existing methods on four cross-domain evaluations.

ICML Conference 2024 Conference Paper

Directly Denoising Diffusion Models

• Dan Zhang
• Jingjing Wang
• Feng Luo

In this paper, we present Directly Denoising Diffusion Models (DDDMs): a simple and generic approach for generating realistic images with few-step sampling, while multistep sampling is still preserved for better performance. DDDMs require no delicately designed samplers nor distillation on pre-trained distillation models. DDDMs train the diffusion model conditioned on an estimated target that was generated from previous training iterations of its own. To generate images, samples generated from previous timestep are also taken into consideration, guiding the generation process iteratively. We further propose Pseudo-LPIPS, a novel metric loss that is more robust to various values of hyperparameter. Despite its simplicity, the proposed approach can achieve strong performance in benchmark datasets. Our model achieves FID scores of 2. 57 and 2. 33 on CIFAR-10 in one-step and two-step sampling respectively, surpassing those obtained from GANs and distillation-based models. By extending the sampling to 1000 steps, we further reduce FID score to 1. 79, aligning with state-of-the-art methods in the literature. For ImageNet 64x64, our approach stands as a competitive contender against leading models.

NeurIPS Conference 2024 Conference Paper

Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection

• Jingjing Wang
• Minhuan Huang
• Yuanpin Nie
• Xiang Li
• Qianjin Du
• Wei Kong
• Huan Deng
• Xiaohui Kuang

Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code comprising multiple functional modules or diverse vulnerability subtypes. In this paper, we present a knowledge fusion-based vulnerability detection method (KF-GVD) that integrates specific vulnerability knowledge into the Graph Neural Network feature learning process. KF-GVD achieves accurate vulnerability detection across different functional modules of the Linux kernel and vulnerability subtypes without compromising general task performance. Extensive experiments demonstrate that KF-GVD outperforms SOTAs on function-level and statement-level vulnerability detection across various target tasks, with an average increase of 40. 9% in precision and 26. 1% in recall. Notably, KF-GVD discovered 9 undisclosed vulnerabilities when employing on C/C++ open-source projects without ground truth.

NeurIPS Conference 2023 Conference Paper

Brant: Foundation Model for Intracranial Neural Signal

• Daoze Zhang
• Zhizhang Yuan
• Yang Yang
• Junru Chen
• Jingjing Wang
• Yafeng Li

We propose a foundation model named Brant for modeling intracranial recordings, which learns powerful representations of intracranial neural signals by pre-training, providing a large-scale, off-the-shelf model for medicine. Brant is the largest model in the field of brain signals and is pre-trained on a large corpus of intracranial data collected by us. The design of Brant is to capture long-term temporal dependency and spatial correlation from neural signals, combining the information in both time and frequency domains. As a foundation model, Brant achieves SOTA performance on various downstream tasks (i. e. neural signal forecasting, frequency-phase forecasting, imputation and seizure detection), showing the generalization ability to a broad range of tasks. The low-resource label analysis and representation visualization further illustrate the effectiveness of our pre-training strategy. In addition, we explore the effect of model size to show that a larger model with a higher capacity can lead to performance improvements on our dataset. The source code and pre-trained weights are available at: https: //zju-brainnet. github. io/Brant. github. io/.

AAAI Conference 2021 Conference Paper

Self-Domain Adaptation for Face Anti-Spoofing

• Jingjing Wang
• Jingyi Zhang
• Ying Bian
• Youyi Cai
• Chunmao Wang
• Shiliang Pu

Although current face anti-spoofing methods achieve promising results under intra-dataset testing, they suffer from poor generalization to unseen attacks. Most existing works adopt domain adaptation (DA) or domain generalization (DG) techniques to address this problem. However, the target domain is often unknown during training which limits the utilization of DA methods. DG methods can conquer this by learning domain invariant features without seeing any target data. However, they fail in utilizing the information of target data. In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference. Specifically, a domain adaptor is designed to adapt the model for test domain. In order to learn a better adaptor, a meta-learning based adaptor learning algorithm is proposed using the data of multiple source domains at the training step. At test time, the adaptor is updated using only the test domain data according to the proposed unsupervised adaptor loss to further improve the performance. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

YNICL Journal 2021 Journal Article

Volume quantification of endolymphatic hydrops in patients with vestibular schwannoma

• Jingjing Wang
• Chunyan Li
• Yaoqian Liu
• Yuxin Tian
• Yuanping Xiong
• Yanmei Feng
• Dongzhen Yu
• Zhengnong Chen

AAAI Conference 2020 Conference Paper

Sentiment Classification in Customer Service Dialogue with Topic-Aware Multi-Task Learning

• Jiancheng Wang
• Jingjing Wang
• Changlong Sun
• Shoushan Li
• Xiaozhong Liu
• Luo Si
• Min Zhang
• Guodong Zhou

Sentiment analysis in dialogues plays a critical role in dialogue data analysis. However, previous studies on sentiment classification in dialogues largely ignore topic information, which is important for capturing overall information in some types of dialogues. In this study, we focus on the sentiment classification task in an important type of dialogue, namely customer service dialogue, and propose a novel approach which captures overall information to enhance the classification performance. Specifically, we propose a topic-aware multi-task learning (TML) approach which learns topicenriched utterance representations in customer service dialogue by capturing various kinds of topic information. In the experiment, we propose a large-scale and high-quality annotated corpus for the sentiment classification task in customer service dialogue and empirical studies on the proposed corpus show that our approach significantly outperforms several strong baselines.

NeurIPS Conference 2019 Conference Paper

Multivariate Triangular Quantile Maps for Novelty Detection

• Jingjing Wang
• Sun Sun
• Yaoliang Yu

Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches. In this work, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our framework unifies and extends many classical and recent novelty detection algorithms, and opens the way to exploit recent advances in flow-based neural density estimation. We adapt the multiple gradient descent algorithm to obtain the first efficient end-to-end implementation of our framework that is free of tuning hyperparameters. Extensive experiments over a number of real datasets confirm the efficacy of our proposed method against state-of-the-art alternatives.

IJCAI Conference 2018 Conference Paper

Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks

• Jingjing Wang
• Jie Li
• Shoushan Li
• Yangyang Kang
• Min Zhang
• Luo Si
• Guodong Zhou

Aspect sentiment classification, a challenging task in sentiment analysis, has been attracting more and more attention in recent years. In this paper, we highlight the need for incorporating the importance degrees of both words and clauses inside a sentence and propose a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification. Specifically, we first adopt sentence-level discourse segmentation to segment a sentence into several clauses. Then, we leverage multiple Bi-directional LSTM layers to encode all clauses and propose a word-level attention layer to capture the importance degrees of words in each clause. Third and finally, we leverage another Bi-directional LSTM layer to encode the outputs from the former layers and propose a clause-level attention layer to capture the importance degrees of all the clauses inside a sentence. Experimental results on the laptop and restaurant datasets from SemEval-2015 demonstrate the effectiveness of our proposed approach to aspect sentiment classification.

AAAI Conference 2017 Conference Paper

Dual-Clustering Maximum Entropy with Application to Classification and Word Embedding

• Xiaolong Wang
• Jingjing Wang
• ChengXiang Zhai

Maximum Entropy (ME), as a general-purpose machine learning model, has been successfully applied to various fields such as text mining and natural language processing. It has been used as a classification technique and recently also applied to learn word embedding. ME establishes a distribution of the exponential form over items (classes/words). When training such a model, learning efficiency is guaranteed by globally updating the entire set of model parameters associated with all items at each training instance. This creates a significant computational challenge when the number of items is large. To achieve learning efficiency with affordable computational cost, we propose an approach named Dual-Clustering Maximum Entropy (DCME). Exploiting the primal-dual form of ME, it conducts clustering in the dual space and approximates each dual distribution by the corresponding cluster center. This naturally enables a hybrid online-offline optimization algorithm whose time complexity per instance only scales as the product of the feature/word vector dimensionality and the cluster number. Experimental studies on text classification and word embedding learning demonstrate that DCME effectively strikes a balance between training speed and model quality, substantially outperforming state-of-the-art methods.

IJCAI Conference 2017 Conference Paper

Joint Learning on Relevant User Attributes in Micro-blog

• Jingjing Wang
• Shoushan Li
• Guodong Zhou

User attribute classification aims to identify users’ attributes (e. g. , gender, age and profession) by leveraging user generated content. However, conventional approaches to user attribute classification focus on single attribute classification involving only one user attribute, which completely ignores the relationship among various user attributes. In this paper, we confront a novel scenario in user attribute classification where relevant user attributes are jointly learned, attempting to make the relevant attribute classification tasks help each other. Specifically, we propose a joint learning approach, namely Aux-LSTM, which first learns a proper auxiliary representation between the related tasks and then leverages the auxiliary representation to integrate the learning process in both tasks. Empirical studies demonstrate the effectiveness of our proposed approach to joint learning on relevant user attributes.

AAAI Conference 2016 Conference Paper

EKNOT: Event Knowledge from News and Opinions in Twitter

• Min Li
• Jingjing Wang
• Wenzhu Tong
• Hongkun Yu
• Xiuli Ma
• Yucheng Chen
• Haoyan Cai
• Jiawei Han

We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion in Twitter, and provides a comprehensive summary of the events from both news media and social media’s point of view. EKNOT takes a time period as input and outputs a complete picture of the events within the given time range along with the public opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an entity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. Also, if a user is interested in a particular event, he/she can zoom into this event to investigate its aspects (subevents) summarized in the same manner. EKNOT is built on real-time crawled news articles and tweets, allowing users to explore the dynamics of major events with minimal delays.

IJCAI Conference 2016 Conference Paper

Learning Hostname Preference to Enhance Search Relevance

• Jingjing Wang
• Changsung Kang
• Yi Chang
• Jiawei Han

Hostnames such as en. wikipedia. org and www. amazon. com are strong indicators of the content they host. The relevant hostnames for a query can be a signature that captures the query intent. In this study, we learn the hostname preference of queries, which are further utilized to enhance search relevance. Implicit and explicit query intent are modeled simultaneously by a feature aware matrix completion framework. A block-wise parallel algorithm was developed on top of the Spark MLlib for fast optimization of feature aware matrix completion. The optimization completes within minutes at the scale of a million x million matrix, which enables efficient experimental studies at the web scale. Evaluation of the learned hostname preference is performed both intrinsically on test errors, and extrinsically on the impact on search ranking relevance. Experimental results demonstrate that capturing hostname preference can significantly boost the retrieval performance.

IJCAI Conference 2015 Conference Paper

Interactive Gender Inference with Integer Linear Programming

• Shoushan Li
• Jingjing Wang
• Guodong Zhou
• Hanxiao Shi