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Fei Jiang

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10 papers

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AAAI Conference 2026 Conference Paper

CLIP2Pose: Frozen CLIP as Semantic Guide for Domain Adaptive Pose Estimation

Jiawen Li
Fei Jiang
Dandan Zhu
Jinxin Shi
Aimin Zhou

Unsupervised domain adaptive pose estimation is a fundamental yet challenging task due to the need to transfer from labeled synthetic data to unlabeled real data. Nevertheless, the underlying pose semantics, which are governed by spatial structure, remain largely consistent across domains. This observation motivates the use of vision-language models, which provide domain-invariant representations that align well with high-level semantic concepts. Motivated by this, we propose CLIP2Pose, a novel framework that leverages the semantic robustness of frozen CLIP encoders to facilitate cross-domain generalization. We first introduce a semantic-driven prompt mechanism that encodes structural priors, domain-specific appearance, and instance-level context into the image representation. This guides the model to focus on semantically meaningful and structurally relevant features. Next, we propose a semantic modulation module that adaptively refines visual features by conditioning them on prompt-derived embeddings, enhancing alignment between semantics and visual patterns. To further bridge the modality and domain gaps, we design a directional alignment loss that encourages consistent structural reasoning across both vision and language representations. Extensive experiments on domain adaptive human body and hand pose benchmarks show that CLIP2Pose achieves state-of-the-art performance.

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EAAI Journal 2026 Journal Article

Global-local contrastive learning: A multi-operating-condition guided approach for few-shot cross-domain bearing fault diagnosis

Yue Zhang
Xinye Chen
Jie Lai
Bin Zhang
Zhuyun Chen
Fei Jiang

Although existing transfer learning approaches have demonstrated their potential in cross-domain bearing fault diagnosis, they predominantly rely on single-source operating conditions and emphasize global feature alignment between domains. This single-domain paradigm introduces two critical limitations: insufficient adaptation of decision boundaries for target domain distributions, and neglect of discriminative local features across varying operational conditions. To overcome these challenges, a multi-operating-condition-guided approach with global-local contrastive learning for few-shot cross-domain fault diagnosis is proposed. Our methodology innovatively integrates multi-source domain supervision with contrastive feature learning through two key mechanisms: global contrastive alignment, which preserves condition-invariant characteristics across multiple operational domains, and local contrastive refinement, which enhances discriminative feature learning through fine-grained sample relationships. By jointly optimizing global and local contrastive objectives, the proposed method effectively bridges domain discrepancies while maintaining condition-specific discriminability, particularly in few-shot scenarios in which only limited labeled target samples are available. Comprehensive evaluations of two rotating machinery datasets demonstrated that the proposed method achieved superior cross-domain diagnostic accuracy and enhanced stability compared with other popular transfer learning methods. https: //github. com/xinyeC/fircode1024cc/tree/main/GLCL.

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YNIMG Journal 2023 Journal Article

Bayesian inference of a spectral graph model for brain oscillations

Huaqing Jin
Parul Verma
Fei Jiang
Srikantan S Nagarajan
Ashish Raj

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

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YNIMG Journal 2023 Journal Article

Dynamic functional connectivity MEG features of Alzheimer’s disease

Huaqing Jin
Kamalini G. Ranasinghe
Pooja Prabhu
Corby Dale
Yijing Gao
Kiwamu Kudo
Keith Vossel
Ashish Raj

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

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IJCAI Conference 2022 Conference Paper

CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning

Di Jin
Luzhi Wang
Yizhen Zheng
Xiang Li
Fei Jiang
Wei Lin
Shirui Pan

Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score. In addition, existing unsupervised graph similarity learning methods are mainly clustering-based, which ignores the valuable information embodied in graph pairs. To this end, we propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning in order to calculate the similarity between any two input graph objects. Specifically, we generate two augmented views for each graph in a pair respectively. Then, we employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning. The former is resorted to strengthen the consistency of node representations in two views. The latter is utilized to identify node differences between different graphs. Finally, we transform node representations into graph-level representations via pooling operations for graph similarity computation. We have evaluated CGMN on eight real-world datasets, and the experiment results show that the proposed new approach is superior to the state-of-the-art methods in graph similarity learning downstream tasks.

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JMLR Journal 2022 Journal Article

Matrix Completion with Covariate Information and Informative Missingness

Huaqing Jin
Yanyuan Ma
Fei Jiang

We study the problem of matrix completion when the missingness of the matrix entries is dependent on the unobserved response values themselves and hence the missingness itself is informative. Furthermore, we allow to take into account the covariate information to establish its relation with the response and hence enable prediction. We devise a novel procedure to simultaneously complete the partially observed matrix and assess the covariate effect. Allowing the matrix dimensions as well as the number of covariates to grow ultra-high, under the classic low-rank matrix and sparse covariate effect assumptions, we rigorously establish the statistical guarantee of our procedure and the algorithmic convergence. The method is demonstrated via simulation studies and is used to analyze a Yelp data set and a MovieLens data set. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

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YNIMG Journal 2022 Journal Article

Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging

Fei Jiang
Huaqing Jin
Yijing Gao
Xihe Xie
Jennifer Cummings
Ashish Raj
Srikantan Nagarajan

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

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IJCAI Conference 2020 Conference Paper

GestureDet: Real-time Student Gesture Analysis with Multi-dimensional Attention-based Detector

Rui Zheng
Fei Jiang
Ruimin Shen

Students’ gestures, hand-raising, stand-up, and sleeping, indicates the engagement of students in classrooms and partially reflects teaching quality. Therefore, fast and automatically recognizing these gestures are of great importance. Due to limited computational resources in primary and secondary schools, we propose a real-time student behavior detector based on light-weight MobileNetV2-SSD to reduce the dependency of GPUs. Firstly, we build a large-scale corpus from real schools to capture various behavior gestures. Based on such a corpus, we transfer the gesture recognition task into object detections. Secondly, we design a multi-dimensional attention-based detector, named GestureDet, for real-time and accurate gesture analysis. The multi-dimensional attention mechanisms simultaneously consider all the dimensions of the training set, aiming to pay more attention to discriminative features and samples that are important for the final performance. Specifically, the spatial attention is constructed with stacked dilated convolution layers to generate a soft and learnable mask for re-weighting foreground and background features; the channel attention introduces the context modeling and squeeze-and-excitation module to focus on discriminative features; the batch attention discriminates important samples with a new designed reweight strategy. Experimental results demonstrate the effectiveness and versatility of GestureDet, which achieves 75. 2% mAP on real student behavior dataset, and 74. 5% on public PASCAL VOC dataset at 20fps on embedding device Nvidia Jetson TX2. Code will be made publicly available.

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NeurIPS Conference 2018 Conference Paper

Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors

Fei Jiang
Guosheng Yin
Francesca Dominici

Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. We speed up the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. Extensive simulation studies are conducted to compare the BMS with existing methods, and our approach is illustrated with application to the magnetic resonance imaging guided radiation therapy data.

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AAAI Conference 2018 Conference Paper

Efficient Multi-Dimensional Tensor Sparse Coding Using t-Linear Combination

Fei Jiang
Xiao-Yang Liu
Hongtao Lu
Ruimin Shen

In this paper, we propose two novel multi-dimensional tensor sparse coding (MDTSC) schemes using the t-linear combination. Based on the t-linear combination, the shifted versions of the bases are used for the data approximation, but without need to store them. Therefore, the dictionaries of the proposed schemes are more concise and the coefﬁcients have richer physical explanations. Moreover, we propose an efﬁcient alternating minimization algorithm, including the tensor coefﬁcient learning and the tensor dictionary learning, to solve the proposed problems. For the tensor coefﬁcient learning, we design a tensor-based fast iterative shrinkage algorithm. For the tensor dictionary learning, we ﬁrst divide the problem into several nearly-independent subproblems in the frequency domain, and then utilize the Lagrange dual to further reduce the number of optimization variables. Experimental results on multi-dimensional signals denoising and reconstruction (3DTSC, 4DTSC, 5DTSC) show that the proposed algorithms are more efﬁcient and outperform the state-of-theart tensor-based sparse coding models.

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