Author name cluster

Jie Yu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

21 papers

2 author rows

EAAI Journal 2026 Journal Article

Dynamic multi-periodic spatio-temporal graph neural network for multivariate time series forecasting

Shuai Zhang
ZhuoLin Li
Jie Yu
LingYu Xu

Existing spatio-temporal graph neural network (GNN) models face several challenges: they primarily rely on fixed-length time windows, which hinders the comprehensive modeling of complex spatio-temporal dependencies. Furthermore, they often process temporal and spatial dimensions in isolation, thus preventing the unified capture of spatio-temporal dependencies. Moreover, these models typically assume static spatial relationships, thereby ignoring their dynamic nature and co-evolution with temporal patterns. To overcome these limitations, we propose the Dynamic Multi-Periodic Spatio-Temporal Graph Neural Network (DM-PSTGNN), aiming to comprehensively and uniformly model spatio-temporal dependencies. Specifically, DM-PSTGNN first employs a dynamic period extraction module based on the Fast Fourier Transform (FFT) to identify latent periodic patterns from the data. The model then considers the spatio-temporal correlations within each period as a whole. It utilizes a dynamic periodic graph learning module, incorporating techniques such as tensor decomposition, to construct dynamic periodic spatio-temporal graphs. Next, a dynamic graph convolution module uniformly captures spatio-temporal dependencies within each period. Finally, a period spatio-temporal fusion module integrates the dependencies across different periods to achieve a comprehensive and unified modeling of dynamic spatio-temporal dependencies in time series data. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed DM-PSTGNN. Furthermore, visualization analysis confirms the interpretability of its learnable graph structure.

Details DOI

AAAI Conference 2026 Conference Paper

Leveraging Image as Compressed Visual Prompt and Hierarchical Visual Knowledge for Effective Image Utilization in MLLMs

Shezheng Song
Kangcheng Ding
Shan Zhao
Shasha Li
Xiaopeng Li
Chengyu Wang
Qian Wan
Bin Ji

Multimodal Large Language Models (MLLMs) integrate text and images for complex reasoning tasks, but efficiently utilizing image remains a challenge due to redundancy and noise. Traditional methods take the entire image features as visual prompt into the MLLMs, leading to excessive visual tokens that disrupt textual information expression. Thus, recent studies treat image features as visual knowledge, storing them in the feed-forward network for retrieval when needed. These methods, completely removing images from the input, may hinder the activation of image-related knowledge. Besides, current visual knowledge focuses on fine-grained details but overlooks the hierarchical process of visual perception. As described in feature integration theory, global structure is first processed before details are integrated. Ignoring this process may lead to a fragmented visual understanding, making it difficult to capture high-level semantic relationships. To overcome these issues, we propose a novel image utilization mechanism in MLLMs. We leverage a compression-based attention mechanism to generate the compressed visual prompt, which not only mitigates the interference of excessively long visual prompts but also preserves crucial visual information necessary for activating knowledge in the MLLM. Furthermore, we extract hierarchical visual features as visual knowledge using wavelet transforms, allowing the model to capture both global structures and fine-grained details. Experiments show that our method achieves state-of-the-art performance.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Robust High-Order Tensor Compressive Sensing Based on M-Estimators

Xiaowei Wang
Jie Yu
Yulong Wang

Tensor Compressive Sensing (TCS) has gained significant attention recently due to its strong ability to preserve the multidimensional structure of data. However, existing TCS methods face three critical challenges: 1) Biased approximation of tensor rank imposed by the convex surrogate Tensor Nuclear Norm (TNN) may interfere with the original low-rank structure of tensor data. 2) Vulnerability to non-Gaussian noise and outliers makes TCS methods highly susceptible to complex noise environments ubiquitous in real-world applications. 3) Most of them are confined to third-order tensors and cannot handle high-order tensor data effectively. Being aware of these, we propose Robust Tensor Compressive Sensing (RTCS) based on M-estimators with three key innovations: 1) We design a novel M-estimator-based low-rank regularizer for high-order tensors, which provides a superior approximation of tensor rank and better preserves the original data structure. 2) RTCS incorporates a robust Welsch estimator that adaptively mitigates the influence of complex noises and outliers in tensor recovery. 3) RTCS is developed to handle high-order tensors, thereby allowing for broader applicability beyond conventional third-order tensors. We further design an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to handle the complex optimization problem. Experiments show that RTCS consistently outperforms existing approaches across various noises.

PDF Details DOI

JBHI Journal 2026 Journal Article

Tumor Contraction-Aware Multi-Sequence MRI Framework for Accurate Post-Ablation Margin Assessment in Hepatocellular Carcinoma

Linan Dong
Hongwei Ge
Jie Yu
Yong Luo
Jinming Hu
Shichen Yu
Ping Liang

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality, and microwave ablation (MWA) is commonly used for patients ineligible for surgical resection. A critical challenge following MWA is the assessment of the ablative margin, which is complicated by non-diffeomorphic deformations introduced by thermal effects during the procedure. This paper proposes a Multi-sequence Distance-guided Complementary Network (MDCNet) that utilizes multi-sequence MRI to quantify the extent of tumor contraction after MWA. To account for the differential contraction responses of liver parenchyma and tumor tissue, we propose a novel distance-aware mask transformation strategy. This method explicitly models the spatial attenuation of MWA energy and approximates the influence of liver parenchyma's linear elastic response on tumor shrinkage, thereby enhancing the spatial adaptiveness of feature weighting. To capture the distinct structural characteristics of liver tissue emphasized by different MRI sequences and to leverage their complementary information, a gated channel fusion module is introduced to dynamically integrate features from delayed-phase and T2-weighted images. To validate the practical effectiveness of our proposed method, we evaluate the ablative margins of 115 HCC patients using a fine-tuned TransMorph model that incorporated tumor contraction predictions generated by MDCNet, and compare the results with radiologist 2D assessments. The registration method enhanced with MDCNet improved tumor deformation accuracy and achieved a higher Youden Index in detecting incomplete ablations. Moreover, MDCNet provides interpretable predictions, thereby facilitating clinical decision support.

Details DOI

EAAI Journal 2025 Journal Article

A three-dimensional dynamic spatial-temporal graph neural network for ocean temperature field prediction

Shuai Zhang
ZhuoLin Li
Xiaoyu He
Jie Yu
LingYu Xu

Accurate prediction of the ocean temperature field is vital for the protection of marine ecosystems under climate change. However, most existing methods only consider temporal changes, ignoring the rich three-dimensional (3D) dynamic spatial characteristics of the ocean temperature field. To improve prediction accuracy, we propose a fine-grained modeling method that uses the spatial correlations in the ocean temperature field, called the 3D Dynamic Spatial-Temporal Graph Neural Network (3D-DSTGN). Specifically, according to the 3D spatial features and dynamic spatial dependencies of the ocean temperature field, we first decompose the spatial correlation of the ocean temperature field into long-term static and short-term dynamic parts through statistical analysis of real ocean temperature datasets. We then build a 3D dynamic graph structure learning module to create static and dynamic graph structures with 3D spatial features to model and capture the corresponding spatial correlations. Next, based on the two graph structures, we apply a dual-mode graph convolution block to fully capture the dynamic spatial dependencies of the ocean temperature field. Furthermore, we use a multi-scale temporal convolution block to capture complex temporal dependencies from historical ocean temperature data. Finally, the dual-mode graph convolution block and the multi-scale temporal convolution block construct the spatio-temporal recurrent module, which extracts complex dynamic spatio-temporal contextual dependencies. On a large-scale real ocean temperature dataset from the sea surface to a subsurface depth of approximately 2, 000 m, 3D-DSTGN outperforms other baselines in experiments across different temporal and spatial scales, effectively modeling the dynamic three-dimensional spatial relationships of the ocean temperature field.

Details DOI

EAAI Journal 2025 Journal Article

Efficient network architecture for target detection in challenging low-light environments

Qiang Liu
Wei Huang
Tao Hu
Xiaoqiu Duan
Jie Yu
Jiahuan Huang
Jianghao Wei

Target detection in low-light and dark conditions presents significant challenges compared to normal scenarios due to blurriness, low contrast, and information loss from shadows and darkness. Color distortion and noise further hinder color-based detection algorithms. This study introduces an improved detection model that incorporates image enhancement techniques specifically designed for low-light conditions. The focus is on preprocessed image restoration to achieve realistic and natural visual quality. Key enhancements include a Multi-level Fine-Grained Feature Prediction Network architecture, enabling layered feature fusion and refined feature extraction. This leads to improved model accuracy, reduced parameters, and a dynamic detection output head that enhances target region precision, ultimately improving overall detection accuracy. Validation on a public dataset reveals a 5. 6% increase in mean average precision, a 6. 2% reduction in computational costs in terms of Floating-point Operations, and a 20% decrease in model parameter size. Our method outperforms mainstream low-light target detection algorithms in terms of performance and versatility.

Details DOI

ICML Conference 2025 Conference Paper

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

Keyue Qiu
Yuxuan Song
Jie Yu
Hongbo Ma
Ziyao Cao
Zhilong Zhang
Yushuai Wu
Mingyue Zheng

Structure-based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51. 3%, Vina Dock -9. 05 and SA 0. 78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x “Me-Better” Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility. Code is available at https: //github. com/AlgoMole/MolCRAFT.

Details

IJCAI Conference 2025 Conference Paper

Flexible Generalized Low-Rank Regularizer for Tensor RPCA

Zhiyang Gong
Jie Yu
Yutao Hu
Yulong Wang

Tensor Robust Principal Component Analysis (TRPCA) has emerged as a powerful technique for low-rank tensor recovery. To achieve better recovery performance, a variety of TNN (Tensor Nuclear Norm) based low-rank regularizers have been proposed case by case, lacking a general and flexible framework. In this paper, we design a novel tensor low-rank regularization framework coined FGTNN (Flexible Generalized Tensor Nuclear Norm). Equipped with FGTNN, we develop the FGTRPCA (Flexible Generalized TRPCA) framework, which has two desirable properties. 1) Generalizability: Many existing TRPCA methods can be viewed as special cases of our framework; 2) Flexibility: Using FGTRPCA as a general platform, we derive a series of new TRPCA methods by tuning a continuous parameter to improve performance. In addition, we develop another novel smooth and low-rank regularizer coined t-FGJP and the resulting SFGTRPCA (Smooth FGTRPCA) method by leveraging the low-rankness and smoothness priors simultaneously. Experimental results on various tensor denoising and recovery tasks demonstrate the superiority of our methods.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Rethinking Residual Distribution in Locate-then-Edit Model Editing

Xiaopeng Li
Shangwen Wang
Shasha Li
Shezheng Song
Bin Ji
Ma Jun
Jie Yu

Model editing enables targeted updates to the knowledge of large language models (LLMs) with minimal retraining. Among existing approaches, locate-then-edit methods constitute a prominent paradigm: they first identify critical layers, then compute residuals at the final critical layer based on the target edit, and finally apply least-squares-based multi-layer updates via $\textbf{residual distribution}$. While empirically effective, we identify a counterintuitive failure mode: residual distribution, a core mechanism in these methods, introduces weight shift errors that undermine editing precision. Through theoretical and empirical analysis, we show that such errors increase with the distribution distance, batch size, and edit sequence length, ultimately leading to inaccurate or suboptimal edits. To address this, we propose the $\textbf{B}$oundary $\textbf{L}$ayer $\textbf{U}$pdat$\textbf{E (BLUE)}$ strategy to enhance locate-then-edit methods. Sequential batch editing experiments on three LLMs and two datasets demonstrate that BLUE not only delivers an average performance improvement of 35. 59\%, significantly advancing the state of the art in model editing, but also enhances the preservation of LLMs' general capabilities. Our code is available at https: //github. com/xpq-tech/BLUE.

PDF Details

AAAI Conference 2025 Conference Paper

SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering

Xiaopeng Li
Shasha Li
Shezheng Song
Huijun Liu
Bin Ji
Xi Wang
Jun Ma
Jie Yu

The general capabilities of large language models (LLMs) make them the infrastructure for various AI applications, but updating their inner knowledge requires significant resources. Recent model editing is a promising technique for efficiently updating a small amount of knowledge of LLMs and has attracted much attention. In particular, local editing methods, which directly update model parameters, are proven suitable for updating small amounts of knowledge. Local editing methods update weights by computing least squares closed-form solutions and identify edited knowledge by vector-level matching in inference, which achieve promising results. However, these methods still require a lot of time and resources to complete the computation. Moreover, vector-level matching lacks reliability, and such updates disrupt the original organization of the model's parameters. To address these issues, we propose a detachable and expandable Subject Word Embedding Altering (SWEA) framework, which finds the editing embeddings through token-level matching and adds them to the subject word embeddings in Transformer input. To get these editing embeddings, we propose optimizing then suppressing fusion method, which first optimizes learnable embedding vectors for the editing target and then suppresses the Knowledge Embedding Dimensions (KEDs) to obtain final editing embeddings. We thus propose SWEAOS method for editing factual knowledge in LLMs. We demonstrate the overall state-of-the-art (SOTA) performance of SWEAOS on the CounterFact and zsRE datasets. To further validate the reasoning ability of SWEAOS in editing knowledge, we evaluate it on the more complex RippleEdits benchmark. The results demonstrate that SWEAOS possesses SOTA reasoning ability.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Towards Verifiable Text Generation with Generative Agent

Bin Ji
Huijun Liu
Mingzhe Du
Shasha Li
Xiaodong Liu
Jun Ma
Jie Yu
See-Kiong Ng

Text generation with citations makes it easy to verify the factuality of Large Language Models’ (LLMs) generations. Existing one-step generation studies expose distinct shortages in answer refinement and in-context demonstration matching. In light of these challenges, we propose R2-MGA, a Retrieval and Reflection Memory-augmented Generative Agent. Specifically, it first retrieves the memory bank to obtain the best-matched memory snippet, then reflects the retrieved snippet as a reasoning rationale, next combines the snippet and the rationale as the best-matched in-context demonstration. Additionally, it is capable of in-depth answer refinement with two specifically designed modules. We evaluate R2-MGA across five LLMs on the ALCE benchmark. The results reveal R2-MGA’ exceptional capabilities in text generation with citations. In particular, compared to the selected baselines, it delivers up to +58.8% and +154.7% relative performance gains on answer correctness and citation quality, respectively. Extensive analyses strongly support the motivations of R2-MGA.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Intentional Evolutionary Learning for Untrimmed Videos with Long Tail Distribution

Yuxi Zhou
Xiujie Wang
Jianhua Zhang
Jiajia Wang
Jie Yu
Hao Zhou
Yi Gao
Shengyong Chen

Human intention understanding in untrimmed videos aims to watch a natural video and predict what the person’s intention is. Currently, exploration of predicting human intentions in untrimmed videos is far from enough. On the one hand, untrimmed videos with mixed actions and backgrounds have a significant long-tail distribution with concept drift characteristics. On the other hand, most methods can only perceive instantaneous intentions, but cannot determine the evolution of intentions. To solve the above challenges, we propose a loss based on Instance Confidence and Class Accuracy (ICCA), which aims to alleviate the prediction bias caused by the long-tail distribution with concept drift characteristics in video streams. In addition, we propose an intention-oriented evolutionary learning method to determine the intention evolution pattern (from what action to what action) and the time of evolution (when the action evolves). We conducted extensive experiments on two untrimmed video datasets (THUMOS14 and ActivityNET v1.3), and our method has achieved excellent results compared to SOTA methods. The code and supplementary materials are available at https://github.com/Jennifer123www/UntrimmedVideo.

PDF Details DOI

AAAI Conference 2024 Conference Paper

PMET: Precise Model Editing in a Transformer

Xiaopeng Li
Shasha Li
Shezheng Song
Jing Yang
Jun Ma
Jie Yu

Model editing techniques modify a minor proportion of knowledge in Large Language Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing methods assume Transformer Layer (TL) hidden states are values of key-value memories of the Feed-Forward Network (FFN). They usually optimize the TL hidden states to memorize target knowledge and use it to update the weights of the FFN in LLMs. However, the information flow of TL hidden states comes from three parts: Multi-Head Self-Attention (MHSA), FFN, and residual connections. Existing methods neglect the fact that the TL hidden states contains information not specifically required for FFN. Consequently, the performance of model editing decreases. To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns. This implies that MHSA weights do not require updating when new knowledge is introduced. Based on above findings, we introduce PMET, which simultaneously optimizes Transformer Component (TC, namely MHSA and FFN) hidden states, while only using the optimized TC hidden states of FFN to precisely update FFN weights. Our experiments demonstrate that PMET exhibits state-of-the-art performance on both the \textsc{counterfact} and zsRE datasets. Our ablation experiments substantiate the effectiveness of our enhancements, further reinforcing the finding that the MHSA encodes certain general knowledge extraction patterns and indicating its storage of a small amount of factual knowledge. Our code is available at \url{https://github.com/xpq-tech/PMET}.

PDF Details DOI

ICLR Conference 2023 Conference Paper

Behavior Prior Representation learning for Offline Reinforcement Learning

Hongyu Zang
Xin Li 0033
Jie Yu
Chen Liu
Riashat Islam
Remi Tachet des Combes
Romain Laroche

Offline reinforcement learning (RL) struggles in environments with rich and noisy inputs, where the agent only has access to a fixed dataset without environment interactions. Past works have proposed common workarounds based on the pre-training of state representations, followed by policy training. In this work, we introduce a simple, yet effective approach for learning state representations. Our method, Behavior Prior Representation (BPR), learns state representations with an easy-to-integrate objective based on behavior cloning of the dataset: we first learn a state representation by mimicking actions from the dataset, and then train a policy on top of the fixed representation, using any off-the-shelf Offline RL algorithm. Theoretically, we prove that BPR carries out performance guarantees when integrated into algorithms that have either policy improvement guarantees (conservative algorithms) or produce lower bounds of the policy values (pessimistic algorithms). Empirically, we show that BPR combined with existing state-of-the-art Offline RL algorithms leads to significant improvements across several offline control benchmarks. The code is available at \url{https://github.com/bit1029public/offline_bpr}

Details

EAAI Journal 2023 Journal Article

Diagnosis of hepatocellular carcinoma using deep network with multi-view enhanced patterns mined in contrast-enhanced ultrasound data

Xiangfei Feng
Wenjia Cai
Rongqin Zheng
Lina Tang
Jianhua Zhou
Hui Wang
Jintang Liao
Baoming Luo

Hepatocellular carcinoma, representing the most frequent primary liver cancer, is a common cancer disease that is the fourth leading cause of cancer-related mortality worldwide. In comparison, non-hepatocellular carcinoma liver cancers often present different prognoses and require distinct management which makes the accurate discrimination between hepatocellular carcinoma and non-hepatocellular carcinoma malignant lesions in contrast-enhanced ultrasound data critical for precise intervention. However, different types of liver cancers have similar enhanced patterns against the perfusion stages that raise the difficulty in the classification of hepatocellular carcinoma with the other liver cancers, especially when the contrast-enhanced ultrasound data is collected from different imaging machines. To this end, this paper innovatively proposes to extract perfusion features from a multi-view learning procedure for obtaining the inherent distinguishing features among liver cancers, leading to a more precise deep model in differentiating the hepatocellular carcinoma from other malignant cases. In particular, the proposed network consists of two novel structures for learning the correlation information among the different views to enhance the robustness of the features and fuse them by reducing redundant information. The proposed method is verified on a multi-source dataset collected from 1241 participants and achieves an AUC value of 89% for classification performance. The experimental results demonstrate the effectiveness of the proposed method for the diagnosis of hepatocellular carcinoma with a multi-source contrast-enhanced ultrasound dataset and might provide an effective assistant for clinical radiologists in liver cancer differentiation.

Details DOI

JBHI Journal 2022 Journal Article

3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition

Shuaiqi Liu
Xu Wang
Ling Zhao
Bing Li
Weiming Hu
Jie Yu
Yu-Dong Zhang

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this paper. The 3DCANN model is composed of spatio-temporal feature extraction module and EEG channel attention weight learning module, which can extract the dynamic relation well among multi-channel EEG signals and the internal spatial relation of multi-channel EEG signals during continuous period time. In this model, the spatio-temporal features are fused with the weights of dual attention learning, and the fused features are input into the softmax classifier for emotion classification. In addition, we utilize SJTU Emotion EEG Dataset (SEED) to appraise the feasibility and effectiveness of the proposed algorithm. Finally, experimental results display that the 3DCANN method has superior performance over the state-of-the-art models in EEG emotion recognition.

Details DOI

NeurIPS Conference 2021 Conference Paper

One Million Scenes for Autonomous Driving: ONCE Dataset

Jiageng Mao
Niu Minzhe
ChenHan Jiang
hanxue liang
Jingheng Chen
Xiaodan Liang
Yamin Li
Chaoqiang Ye

Current perception models in autonomous driving have become notorious for greatly relying on a mass of annotated data to cover unseen cases and address the long-tail problem. On the other hand, learning from unlabeled large-scale collected data and incrementally self-training powerful recognition models have received increasing attention and may become the solutions of next-generation industry-level powerful and robust perception models in autonomous driving. However, the research community generally suffered from data inadequacy of those essential real-world scene data, which hampers the future exploration of fully/semi/self-supervised methods for 3D perception. In this paper, we introduce the ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario. The ONCE dataset consists of 1 million LiDAR scenes and 7 million corresponding camera images. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available (\eg nuScenes and Waymo), and it is collected across a range of different areas, periods and weather conditions. To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset. We conduct extensive analyses on those methods and provide valuable observations on their performance related to the scale of used data. Data, code, and more information are available at \href{https: //once-for-auto-driving. github. io/index. html}{http: //www. once-for-auto-driving. com}.

PDF Details

JBHI Journal 2020 Journal Article

Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics

Qinghua Huang
Fengxin Pan
Wei Li
Feiniu Yuan
Hangtong Hu
Jinhua Huang
Jie Yu
Wei Wang

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94. 40%, recall rate 94. 76%, F1-score value 94. 62%, specificity 93. 62% and sensitivity 94. 76%, indicating good merit for automatically diagnosing atypical HCC cases.

Details DOI

AAAI Conference 2017 Short Paper

Natural Language Person Retrieval

Tao Zhou
Jie Yu

Following the recent progress in image classification and image captioning using deep learning, we developed a novel person retrieval system using natural language, which to our knowledge is first of its kind. Our system employs a state-of-the-art deep learning based natural language object retrieval framework to detect and retrieve people in images. Quantitative experimental results show significant improvement over state-of-the-art meth- ods for generic object retrieval. This line of research provides great advantages for searching large amounts of video surveil- lance footage and it can also be utilized in other domains, such as human-robot interaction.

PDF Details

EAAI Journal 2013 Journal Article

A new fault diagnosis method of multimode processes using Bayesian inference based Gaussian mixture contribution decomposition

Jie Yu

This paper presents a novel Bayesian inference based Gaussian mixture contribution (BIGMC) method to isolate and diagnose the faulty variables in chemical processes with multiple operating modes. The statistical confidence intervals of traditional principal component analysis (PCA) based T 2 and SPE diagnostics rely upon the assumption that the operating data follow a multivariate Gaussian distribution approximately and therefore may not be able to determine the faulty variables in multimode non-Gaussian processes accurately. As an alternative solution, the proposed BIGMC method first identifies the multiple Gaussian modes corresponding to different operating conditions and then integrates the Mahalanobis distance based variable contributions across all the Gaussian clusters through Bayesian inference strategy. The derived BIGMC index is of probabilistic feature and includes all operation scenarios with posterior probabilities as weighting factors. The Tennessee Eastman process (TEP) is used to demonstrate the utility of the proposed BIGMC method for fault diagnosis of multimode processes. The comparison of the single-PCA and multi-PCA based contribution approaches shows that the BIGMC method can effectively identify the leading faulty variables with superior diagnosis capability.

Details DOI

TIST Journal 2011 Journal Article

Collection-based sparse label propagation and its application on social group suggestion from photos

Jie Yu
Xin Jin
Jiawei Han
Jiebo Luo

Online social network services pose great opportunities and challenges for many research areas. In multimedia content analysis, automatic social group recommendation for images holds the promise to expand one's social network through media sharing. However, most existing techniques cannot generate satisfactory social group suggestions when the images are classified independently. In this article, we present novel methods to produce accurate suggestions of suitable social groups from a user's personal photo collection. First, an automatic clustering process is designed to estimate the group similarities, select the optimal number of clusters and categorize the social groups. Both visual content and textual annotations are integrated to generate initial predictions of the group categories for the images. Next, the relationship among images in a user's collection is modeled as a sparse graph. A collection-based sparse label propagation method is proposed to improve the group suggestions. Furthermore, the sparse graph-based collection model can be readily exploited to select the most influential and informative samples for active relevance feedback, which can be integrated with the label propagation process without the need for classifier retraining. The proposed methods have been tested on group suggestion tasks for real user collections and demonstrated superior performance over the state-of-the-art techniques.

Details DOI