Si Chen

AAAI Conference 2026 Conference Paper

Fair Facial Attribute Recognition via Group-Decoupled Vision Transformer with Mask-Guided Correlation Suppression

• Huichang Huang
• Kunchi Li
• Si Chen
• Da-Han Wang

Facial Attribute Recognition (FAR) holds significant potential for wide-ranging applications. However, traditionally trained FAR models exhibit unfairness, largely due to data bias—where certain sensitive attributes correlate statistically with target attributes. To address this, we propose a group-attention mechanism: first, each image is categorized into subgroups (e.g., Male/Female&short hair, Male/Female&long hair). Within the attention mechanism, distinct Query parameters are used for each group, with shared Key and Value parameters. As group-specific Query parameters are trained on subgrouped data, the noted bias is effectively mitigated. Consequently, integrating this Group-Attention into Vision Transformer (ViT) yields our novel Group-Decoupled ViT (GD-ViT) model. Moreover, to further attenuate the statistical correlation between sensitive and target attributes, we propose a Mask-Guided Correlation Suppression learning strategy. Specifically, in Stage 1, it first leverages a min-max dual-loss optimization strategy to train GD-ViT in capturing key regions related to sensitive attributes yet irrelevant to target attributes. Then, in Stage 2, it trains another GD-ViT by masking sensitive regions identified in Stage 1, fusing the masked output (as intermediate input) with the model’s intermediate outputs. This weakens regions associated with sensitive attributes while enhancing others, suppressing the learning of key features related to sensitive attributes. Consequently, it encourages the model to focus more on intrinsic target attribute regions and balances the learning process between the sensitive attribute and the target attribute. Extensive experiments demonstrate that our method achieves superior performance across three benchmark datasets for fair facial attribute recognition.

EAAI Journal 2026 Journal Article

Intellectual property protection for deep learning model and dataset intelligence

• Yongqi Jiang
• Yansong Gao
• Chunyi Zhou
• Hongsheng Hu
• Si Chen
• Anmin Fu
• Willy Susilo

JBHI Journal 2025 Journal Article

Accurate Cobb Angle Estimation via SVD-Based Curve Detection and Vertebral Wedging Quantification

• Chang Shi
• Nan Meng
• Yipeng Zhuang
• Jason Pui Yin Cheung
• Moxin Zhao
• Hua Huang
• Xiuyuan Chen
• Cong Nie

Adolescent idiopathic scoliosis (AIS) is a common spinal malalignment affecting approximately 2. 2% of boys and 4. 8% of girls worldwide. The Cobb angle serves as the gold standard for AIS severity assessment, yet traditional manual measurements suffer from significant observer variability, compromising diagnostic accuracy. Despite prior automation attempts, existing methods use simplified spinal models and predetermined curve patterns that fail to address clinical complexity. We present a novel deep learning framework for AIS assessment that simultaneously predicts both superior and inferior endplate angles with corresponding midpoint coordinates for each vertebra, preserving the anatomical reality of vertebral wedging in progressive AIS. Our approach combines an HRNet backbone with Swin-Transformer modules and biomechanically informed constraints for enhanced feature extraction. We employ Singular Value Decomposition (SVD) to analyze angle predictions directly from vertebral morphology, enabling flexible detection of diverse scoliosis patterns without predefined curve assumptions. Using 630 full-spine anteroposterior radiographs from patients aged 10-18 years with rigorous dual-rater annotation, our method achieved 83. 45% diagnostic accuracy and 2. 55 $^{\circ }$ mean absolute error. The framework demonstrates exceptional generalization capability on out-of-distribution cases. Additionally, we introduce the Vertebral Wedging Index (VWI), a novel metric quantifying vertebral deformation. Longitudinal analysis revealed VWI’s significant prognostic correlation with curve progression while traditional Cobb angles showed no correlation, providing robust support for early AIS detection, personalized treatment planning, and progression monitoring.

NeurIPS Conference 2025 Conference Paper

BeyondMix: Leveraging Structural Priors and Long-Range Dependencies for Domain-Invariant LiDAR Segmentation

• Yujia Chen
• Rui Sun
• Wangkai Li
• Huayu Mai
• Si Chen
• Zhuoyuan Li
• Zhixin Cheng
• Tianzhu Zhang

Domain adaptation for LiDAR semantic segmentation remains challenging due to the complex structural properties of point cloud data. While mix-based paradigms have shown promise, they often fail to fully leverage the rich structural priors inherent in 3D LiDAR point clouds. In this paper, we identify three critical yet underexploited structural priors: permutation invariance, local consistency, and geometric consistency. We introduce BeyondMix, a novel framework that harnesses the capabilities of State Space Models (specifically Mamba) to construct and exploit these structural priors while modeling long-range dependencies that transcend the limited receptive fields of conventional voxel-based approaches. By employing space-filling curves to impose sequential ordering on point cloud data and implementing strategic spatial partitioning schemes, BeyondMix effectively captures domain-invariant representations. Extensive experiments on challenging LiDAR semantic segmentation benchmarks demonstrate that our approach consistently outperforms existing state-of-the-art methods, establishing a new paradigm for unsupervised domain adaptation in 3D point cloud understanding.

AAAI Conference 2025 Conference Paper

Exploring the Better Multimodal Synergy Strategy for Vision-Language Models

• Xiaotian Yin
• Xin Liu
• Si Chen
• Yuan Wang
• Yuwen Pan
• Tianzhu Zhang

Vision-Language models (VLMs) have shown great potential in enhancing open-world visual concept comprehension. Recent researches focus on an optimum multimodal collaboration strategy that significantly advances CLIP-based few-shot tasks. However, existing prompt-based solutions suffer from unidirectional information flow and increased parameters since they explicitly condition the vision prompts on textual prompts across different transformer layers using non-shareable coupling functions. To address this issue, we propose a Dual-shared mechanism based on LoRA (DsRA) that addresses VLM adaptation in low-data regimes. The proposed DsRA enjoys several merits. First, we design an inter-modal shared coefficient that focuses on capturing visual and textual shared patterns, ensuring effective mutual synergy between image and text features. Second, an intra-modal shared matrix is proposed to achieve efficient parameter fine-tuning by combining the different coefficients to generate layer-wise adapters placed in encoder layers. Our extensive experiments demonstrate that DsRA improves the generalizability under few-shot classification, base-to-new generalization, and domain generalization settings. Our code will be released soon.

ICLR Conference 2025 Conference Paper

GeoILP: A Synthetic Dataset to Guide Large-Scale Rule Induction

• Si Chen
• Richong Zhang
• Xu Zhang

Inductive logic programming (ILP) is a machine learning approach aiming to learn explanatory rules from data. While existing ILP systems can successfully solve small-scale tasks, large-scale applications with various language biases are rarely explored. Besides, it is crucial for a large majority of current ILP systems to require expert-defined language bias, which hampers the development of ILP towards broader utilizations. In this paper, we introduce GeoILP, a large-scale synthetic dataset of diverse ILP tasks involving numerous aspects of language bias. These tasks are built from geometry problems, at the level from textbook exercise to regional International Mathematical Olympiad (IMO), with the help of a deduction engine. These problems are elaborately selected to cover all challenging language biases, such as recursion, predicate invention, and high arity. Experimental results show that no existing method can solve GeoILP tasks. In addition, along with classic symbolic-form data, we provide image-form data to boost the development of the joint learning of neural perception and symbolic rule induction.

NeurIPS Conference 2025 Conference Paper

HypoBootstrap: A Bootstrapping Framework for Inductive Reasoning

• Si Chen
• Yifei Li
• Richong Zhang

Inductive reasoning infers general rules from observed evidence, which is one of the most critical intelligence abilities. Previous works have succeeded in formal languages but suffer from onerous and error-prone conversions between a particular formal language and the working language. As large language models (LLMs) have emerged, direct reasoning with various kinds of languages, especially natural languages, without formal language involvement has become feasible. However, existing LLM-based inductive reasoning usually relies on LLM's intrinsic generation ability, which is prone to LLM's hallucination and lacks systematic guidance according to the nature of inductive reasoning. To this end, we propose HypoBootstrap, an integrated framework for inductive reasoning that generates and confirms hypotheses both in a bootstrapping manner. Regarding hypothesis generation, we propose a novel bootstrapping generation strategy, bootstrapping object hypotheses, relational hypotheses, and functional hypotheses successively, which assists LLM in observing the evidence from trivial patterns to non-trivial patterns. Regarding hypothesis confirmation, we utilize Glymour's theory of bootstrap confirmation, a hypothesis confirmation theory from the philosophy of science that can confirm a set of hypotheses. We use its principles to confirm the object hypotheses, relational hypotheses, and functional hypotheses. Empirical studies on four inductive reasoning scenarios of different natures, involving causal induction, concept learning, grammar learning, and abstract reasoning, demonstrate that HypoBootstrap significantly outperforms existing methods.

YNIMG Journal 2025 Journal Article

Olfactory sensation emotion regulation: The implicit emotion regulation function of positive olfactory stimuli during emotional picture processing

• Jiaotao Cai
• Xinran Wang
• Jiayi Zhou
• Ye di
• Ziruo Shen
• Shuo An
• Bingyang Long
• Yicheng Wang

AAAI Conference 2025 Conference Paper

SocialSim: Towards Socialized Simulation of Emotional Support Conversation

• Zhuang Chen
• Yaru Cao
• Guanqun Bi
• Jincenzi Wu
• Jinfeng Zhou
• Xiyao Xiao
• Si Chen
• Hongning Wang

Emotional support conversation (ESC) helps reduce people's psychological stress and provide emotional value through interactive dialogues. Due to the high cost of crowdsourcing a large ESC corpus, recent attempts use large language models for dialogue augmentation. However, existing approaches largely overlook the social dynamics inherent in ESC, leading to less effective simulations. In this paper, we introduce SocialSim, a novel framework that simulates ESC by integrating key aspects of social interactions: social disclosure and social awareness. On the seeker side, we facilitate social disclosure by constructing a comprehensive persona bank that captures diverse and authentic help-seeking scenarios. On the supporter side, we enhance social awareness by eliciting cognitive reasoning to generate logical and supportive responses. Building upon SocialSim, we construct SSConv, a large-scale synthetic ESC corpus of which quality can even surpass crowdsourced ESC data. We further train a chatbot on SSConv and demonstrate its state-of-the-art performance in both automatic and human evaluations. We believe SocialSim offers a scalable way to synthesize ESC, making emotional care more accessible and practical.

YNIMG Journal 2025 Journal Article

Theta oscillations between the ventromedial prefrontal cortex and amygdala support dynamic representations of threat and safety

• Pingping Lu
• Dong Chen
• Wenran Xia
• Si Chen
• Zheng Tan
• Wenjing Zhou
• Liang Wang

TMLR Journal 2024 Journal Article

Data-Centric Defense: Shaping Loss Landscape with Augmentations to Counter Model Inversion

• Si Chen
• Feiyang Kang
• Nikhil Abhyankar
• Ming Jin
• Ruoxi Jia

Machine Learning models have shown susceptibility to various privacy attacks, with model inversion (MI) attacks posing a significant threat. Current defense techniques are mostly \emph{model-centric}, involving modifying model training or inference. However, these approaches require model trainers' cooperation, are computationally expensive, and often result in a significant privacy-utility tradeoff. To address these limitations, we propose a novel \emph{data-centric} approach to mitigate MI attacks. Compared to traditional model-centric techniques, our approach offers the unique advantage of enabling each individual user to control their data's privacy risk, aligning with findings from a Cisco survey that only a minority actively seek privacy protection. Specifically, we introduce several privacy-focused data augmentations that modify the private data uploaded to the model trainer. These augmentations shape the resulting model's loss landscape, making it challenging for attackers to generate private target samples. Additionally, we provide theoretical analysis to explain why such augmentations can reduce the risk of model inversion. We evaluate our approach against state-of-the-art MI attacks and demonstrate its effectiveness and robustness across various model architectures and datasets. Specifically, in standard face recognition benchmarks, we reduce face reconstruction success rates to $\leq5\%$, while maintaining high utility with only a 2\% classification accuracy drop, significantly surpassing state-of-the-art model-centric defenses. This is the first study to propose a data-centric approach for mitigating model inversion attacks, showing promising potential for decentralized privacy protection.

AAAI Conference 2024 Conference Paper

High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-identification

• Liuxiang Qiu
• Si Chen
• Yan Yan
• Jing-Hao Xue
• Da-Han Wang
• Shunzhi Zhu

Visible-infrared person re-identification (VI-ReID) aims to retrieve images of the same persons captured by visible (VIS) and infrared (IR) cameras. Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR images. To address the above problems, we propose a novel high-order structure based middle-feature learning network (HOS-Net) for effective VI-ReID. Specifically, we first leverage a short- and long-range feature extraction (SLE) module to effectively exploit both short-range and long-range features. Then, we propose a high-order structure learning (HSL) module to successfully model the high-order relationship across different local features of each person image based on a whitened hypergraph network. This greatly alleviates model collapse and enhances feature representations. Finally, we develop a common feature space learning (CFL) module to learn a discriminative and reasonable common feature space based on middle features generated by aligning features from different modalities and ranges. In particular, a modality-range identity-center contrastive (MRIC) loss is proposed to reduce the distances between the VIS, IR, and middle features, smoothing the training process. Extensive experiments on the SYSU-MM01, RegDB, and LLCM datasets show that our HOS-Net achieves superior state-of-the-art performance. Our code is available at https://github.com/Jaulaucoeng/HOS-Net.

JBHI Journal 2023 Journal Article

A Privacy-Preserving Medical Data Sharing Scheme Based on Blockchain

• Guangquan Xu
• Chen Qi
• Wenyu Dong
• Lixiao Gong
• Shaoying Liu
• Si Chen
• Jian Liu
• Xi Zheng

With the increasing penetration of the Internet of things (IoT) into people's lives, the limitations of traditional medical systems are emerging. First, the typical way of handling sensitive information can easily lead to privacy disclosure. Second, the medical system is relatively isolated. It is difficult for one medical system to share data with another, and the scope of users' activities is limited within the system boundary. To solve these two problems, we propose a new privacy-preserving medical data-sharing scheme by introducing the authorization mechanism and attribute-based encryption (ABE) based on blockchain, which breaks system boundaries and realizes data sharing among several medical institutions. ABE is used to realize scalable access control. In addition, doctors can share their knowledge to diagnose users by introducing many-to-many matching, which means that patients' health data can be represented by multiple keywords and doctors' expertise can be represented by multiple interests. We provide the correctness and security analysis of our scheme and implement a prototype tool on Ethereum. The experimental results show that our scheme solves the contradiction between the privacy preservation of medical data and the necessity of data sharing.

TMLR Journal 2023 Journal Article

One-Round Active Learning through Data Utility Learning and Proxy Models

• Jiachen T. Wang
• Si Chen
• Ruoxi Jia

While active learning (AL) techniques have demonstrated the potential to produce high-performance models with fewer labeled data, their application remains limited due to the necessity for multiple rounds of interaction with annotators. This paper studies the problem of one-round AL, which aims at selecting a subset of unlabeled points and querying their labels \emph{all at once}. A fundamental challenge is how to measure the utility of different choices of labeling queries for learning a target model. Our key idea is to learn such a utility metric from a small initial labeled set. We demonstrate that our approach leads to state-of-the-art performance on various AL benchmarks and is more robust to the lack of initial labeled data. In addition to algorithmic development and evaluation, we introduce a novel metric for quantifying `\emph{utility transferability}' -- the degree of correlation between the performance changes of two learning algorithms due to variations in training data selection. Previous studies have often observed a notable utility transferability between models, even those with differing complexities. Such transferability enabled our approach, as well as other techniques such as coresets, hyperparameter tuning, and data valuation, to scale up to more sophisticated target models by substituting them with smaller proxy models. Nevertheless, utility transferability has not yet been rigorously defined within a formal mathematical framework, a gap that our work addresses innovatively. We further propose two Monte Carlo-based methods for efficiently comparing utility transferability for different proxy models, thereby facilitating a more informed selection of proxy models.

TMLR Journal 2023 Journal Article

Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion

• Si Chen
• Yi Zeng
• Won Park
• Jiachen T. Wang
• Xun Chen
• Lingjuan Lyu
• Zhuoqing Mao
• Ruoxi Jia

The effectiveness of many existing techniques for removing backdoors from machine learning models relies on access to clean in-distribution data. However, given that these models are often trained on proprietary datasets, it may not be practical to assume that in-distribution samples will always be available. On the other hand, model inversion techniques, which are typically viewed as privacy threats, can reconstruct realistic training samples from a given model, potentially eliminating the need for in-distribution data. To date, the only prior attempt to integrate backdoor removal and model inversion involves a simple combination that produced very limited results. This work represents a first step toward a more thorough understanding of how model inversion techniques could be leveraged for effective backdoor removal. Specifically, we seek to answer several key questions: What properties must reconstructed samples possess to enable successful defense? Is perceptual similarity to clean samples enough, or are additional characteristics necessary? Is it possible for reconstructed samples to contain backdoor triggers? We demonstrate that relying solely on perceptual similarity is insufficient for effective defenses. The stability of model predictions in response to input and parameter perturbations also plays a critical role. To address this, we propose a new bi-level optimization based framework for model inversion that promotes stability in addition to visual quality. Interestingly, we also find that reconstructed samples from a pre-trained generator's latent space do not contain backdoors, even when signals from a backdoored model are utilized for reconstruction. We provide a theoretical analysis to explain this observation. Our evaluation shows that our stabilized model inversion technique achieves state-of-the-art backdoor removal performance without requiring access to clean in-distribution data. Furthermore, its performance is on par with or even better than using the same amount of clean samples.

UAI Conference 2022 Conference Paper

SASH: Efficient secure aggregation based on SHPRG for federated learning

• Zizhen Liu
• Si Chen
• Jing Ye 0001
• Junfeng Fan
• Huawei Li 0001
• Xiaowei Li 0001

To prevent private training data leakage in Federated Learning systems, we propose a novel secure aggregation scheme based on seed homomorphic pseudo-random generator (SHPRG), named SASH. SASH leverages the homomorphic property of SHPRG to simplify the masking and demasking scheme, which for each of the clients and for the server, entails a overhead linear w. r. t model size and constant w. r. t number of clients. We prove that even against worst-case colluding adversaries, SASH preserves training data privacy, while being resilient to dropouts without extra overhead. We experimentally demonstrate SASH significantly improves the efficiency to 20× over baseline, especially in the more realistic case where the numbers of clients and model size become large, and a certain percentage of clients drop out from the system.

AAAI Conference 2022 Conference Paper

When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework

• Xinyi Zou
• Yan Yan
• Jing-Hao Xue
• Si Chen
• Hanzi Wang

Human emotions involve basic and compound facial expressions. However, current research on facial expression recognition (FER) mainly focuses on basic expressions, and thus fails to address the diversity of human emotions in practical scenarios. Meanwhile, existing work on compound FER relies heavily on abundant labeled compound expression training data, which are often laboriously collected under the professional instruction of psychology. In this paper, we study compound FER in the cross-domain few-shot learning setting, where only a few images of novel classes from the target domain are required as a reference. In particular, we aim to identify unseen compound expressions with the model trained on easily accessible basic expression datasets. To alleviate the problem of limited base classes in our FER task, we propose a novel Emotion Guided Similarity Network (EGS-Net), consisting of an emotion branch and a similarity branch, based on a two-stage learning framework. Specifically, in the first stage, the similarity branch is jointly trained with the emotion branch in a multi-task fashion. With the regularization of the emotion branch, we prevent the similarity branch from overfitting to sampled base classes that are highly overlapped across different episodes. In the second stage, the emotion branch and the similarity branch play a “two-student game” to alternately learn from each other, thereby further improving the inference ability of the similarity branch on unseen compound expressions. Experimental results on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed method against several stateof-the-art methods.

AAAI Conference 2019 Conference Paper

Non-Compensatory Psychological Models for Recommender Systems

• Chen Lin
• Xiaolin Shen
• Si Chen
• Muhua Zhu
• Yanghua Xiao

The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i. e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models. Our general assumptions are (1) there are K universal hidden aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models.