Lunke Fei

AAAI Conference 2025 Conference Paper

DiffusionREC: Diffusion Model with Adaptive Condition for Referring Expression Comprehension

• Jingcheng Ke
• Waikeung Wong
• Jia Wang
• Mu Li
• Lunke Fei
• Jie Wen

The objective of referring expression comprehension (REC) is to accurately identify the object in an image described by a given expression. Existing REC methods, including transformer-based and graph-based approaches among others, have shown robust performance in REC tasks. In this study, we present a groundbreaking framework named DiffusionREC for REC task. This framework reimagines the REC task as a text guided bounding box denoising diffusion process, through which noisy bounding boxes are refined and distilled to pinpoint the target box. Throughout the training process, the bounding box of the target object diffuses from its ground-truth position towards a random distribution. Simultaneously, a filtering-based object decoder is introduced to reverse this diffusion of noise, conditional on the provided expression, the result from previous denoised step and the interaction between the expression and the image. At the inference stage, we begin by randomly generating a collection of boxes. Subsequently, the filtering-based object decoder is iteratively employed to refine and prune these bounding boxes, taking into account the conditions on the given expression, the results from the previous denoised step, and the interaction between the expression and the image. Extensive experiments conducted on six datasets demonstrate that DiffusionREC outperforms previous REC methods, yielding superior performances.

IJCAI Conference 2025 Conference Paper

High-Confident Local Structure Guided Consensus Graph Learning For Incomplete Multi-view Clustering

• Shuping Zhao
• Lunke Fei
• Qi Lai
• Jie Wen
• Jinrong Cui
• Tingting Chai

Current existing clustering methods for handling incomplete multi-view data primarily concentrate on learning a common representation or graph from the available views, while overlooking the latent information contained in the missing views and the imbalance of information among different views. Furthermore, instances with weak discriminative features usually degrading the precision of consistent representation or graph across all views. To address these problems, in this paper, we propose a simple but efficient method, called high-confident local structure guided consensus graph learning for incomplete multi-view clustering (HLSCG_IMC). Specifically, this method can adaptively learn a strict block diagonal structure from the available samples using a block diagonal representation regularizer. Different from the existing methods using a simple pairwise affinity graph for structure construction, we consider the influence of instances located at the edge of two clusters on the construction of graph for each view. By harnessing the proposed high-confident strict block diagonal structures, the approach seeks to directly guide the learning of the robust consensus graph. A number of experiments have been conducted to verify the efficacy of our approach.

EAAI Journal 2024 Journal Article

A high-efficiency local and global detector for diatom-based drowning diagnosis

• Jiehang Deng
• Jianfa Yang
• Haomin Wei
• Guosheng Gu
• Qingqing Xiang
• Yukun Du
• Chao Liu
• Zhiqiang Chen

AAAI Conference 2024 Conference Paper

Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures

• Gehui Xu
• Jie Wen
• Chengliang Liu
• Bing Hu
• Yicheng Liu
• Lunke Fei
• Wei Wang

Incomplete multi-view clustering (IMVC) aims to reveal shared clustering structures within multi-view data, where only partial views of the samples are available. Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputation-free methods are susceptible to unbalanced information among views and fail to fully exploit shared information. To address these issues, we propose a novel method based on variational autoencoders. Specifically, we adopt multiple view-specific encoders to extract information from each view and utilize the Product-of-Experts approach to efficiently aggregate information to obtain the common representation. To enhance the shared information in the common representation, we introduce a coherence objective to mitigate the influence of information imbalance. By incorporating the Mixture-of-Gaussians prior information into the latent representation, our proposed method is able to learn the common representation with clustering-friendly structures. Extensive experiments on four datasets show that our method achieves competitive clustering performance compared with state-of-the-art methods.

ICML Conference 2024 Conference Paper

Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering

• Jie Wen 0001
• Shijie Deng
• Waikeung Wong
• Guoqing Chao
• Chao Huang 0008
• Lunke Fei
• Yong Xu 0001

As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods.

EAAI Journal 2023 Journal Article

Joint multi-type feature learning for multi-modality FKP recognition

• Yeping Yang
• Lunke Fei
• Adel Homoud Alshehri
• Shuping Zhao
• Weijun Sun
• Shaohua Teng

AAAI Conference 2023 Conference Paper

Tensorized Incomplete Multi-View Clustering with Intrinsic Graph Completion

• Shuping Zhao
• Jie Wen
• Lunke Fei
• Bob Zhang

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete multi-view clustering with intrinsic graph completion (TIMVC_IGC) is proposed. Firstly, owing to the effectiveness of the low-rank representation in revealing the inherent structure of the data, we exploit it to infer the missing instances and construct the complete graph for each view. Afterwards, inspired by the structural consistency, a between-view consistency constraint is imposed to guarantee the similarity of the graphs from different views. More importantly, the TIMVC_IGC simultaneously learns the low-rank structures of the different views and explores the correlations of the different graphs in a latent manifold sub-space using a low-rank tensor constraint, such that the intrinsic graphs of the different views can be obtained. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. Experimental results on several real-world databases illustrates that the proposed method can outperform the other state-of-the-art related methods for incomplete multi-view clustering.

AAAI Conference 2021 Conference Paper

Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring

• Jie Wen
• Zheng Zhang
• Zhao Zhang
• Lei Zhu
• Lunke Fei
• Bob Zhang
• Yong Xu

In this paper, we propose a novel method, referred to as incomplete multi-view tensor spectral clustering with missingview inferring (IMVTSC-MVI) to address the challenging multi-view clustering problem with missing views. Different from the existing methods which commonly focus on exploring the certain information of the available views while ignoring both of the hidden information of the missing views and the intra-view information of data, IMVTSC-MVI seeks to recover the missing views and explore the full information of such recovered views and available views for data clustering. In particular, IMVTSC-MVI incorporates the feature space based missing-view inferring and manifold space based similarity graph learning into a unified framework. In such a way, IMVTSC-MVI allows these two learning tasks to facilitate each other and can well explore the hidden information of the missing views. Moreover, IMVTSC-MVI introduces the low-rank tensor constraint to capture the high-order correlations of multiple views. Experimental results on several datasets demonstrate the effectiveness of IMVTSC-MVI for incomplete multi-view clustering.

IJCAI Conference 2020 Conference Paper

CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

• Jie Wen
• Zheng Zhang
• Yong Xu
• Bob Zhang
• Lunke Fei
• Guo-Sen Xie

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, \emph{i. e. }, learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.

AAAI Conference 2019 Conference Paper

Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering

• Jie Wen
• Zheng Zhang
• Yong Xu
• Bob Zhang
• Lunke Fei
• Hong Liu

Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored; 3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.