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Bob Zhang

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17 papers

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AAAI Conference 2026 Conference Paper

CondDiff-AMO: Integrating Conditional Diffusion Mechanism for Unified Amodal Mask Generation

CaiJie Zhao
Bob Zhang

Aiming to estimate the full extent of partially occluded objects, amodal segmentation is a critical capability for visual intelligence. Existing methods suffer from limitations in efficiency and precision, due to their reliance on auxiliary information or two-stage architectures. Furthermore, they lack generalizability, failing to meet practical requirements. To overcome these challenges, we proposed a new paradigm, CondDiff-AMO, that interprets amodal segmentation as a denoising problem by leveraging diffusion models. Methodologically, the designed novel framework consists of three key innovations to adapt the task characteristics and unlocks the diffusion models’ potential in amodal segmentation, including a masking strategy in the forward process, an adaptive transformer for conditional feature extraction, and visual-guided sampling. In the forward process, progressive masking strategy converts ground-truth masks to visible masks, simulating amodal segmentation process to enhance reasoning regarding occluded areas. For architectural design, a pyramid network with feature refinement extracts adaptive and representative conditional priors, improving the guidance in the denoising process of diffusion models. As for the sampling stage, a visible mask is incorporated with an ensemble strategy, restricting the prediction on occluded part. Experiments were conducted on five well-known datasets under supervised and zero-shot learning, with the results confirming that CondDiff-AMO outperforms state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework

Zhihao Hao
Bob Zhang
Haisheng Li

Solutions to time-varying problems are crucial for research areas such as predicting changes in human body shape over time. While recurrent neural networks have made significant advancements in this field, their reliance on centralized processing has led to challenges such as model silos and data isolation. In response, distributed AI systems like federated learning have emerged to facilitate dynamic collaboration among models; however, they still depend on central coordinators, which pose risks to system security and efficiency. Moreover, traditional federated learning primarily supports homogeneous models and lacks effective strategies for the interaction of heterogeneous models. To address these limitations, we propose a novel method called Dynamic Collaboration of Heterogeneous Models (DCHM), based on Isomerism Learning, which leverages a consortium blockchain network to enhance model credibility and facilitate coordination among heterogeneous models. Additionally, we introduce a Distributed Hierarchical Aggregation (DHA) algorithm that enables permissioned nodes within each group to aggregate local model results and share them for standardized processing. After several iterative cycles, these nodes perform secondary integration of local results to produce global outcomes. Experimental results demonstrate that DCHM effectively analyzes the temporal variability of body shape changes with high efficiency.

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IJCAI Conference 2025 Conference Paper

Omni-Dimensional State Space Model-driven SAM for Pixel-level Anomaly Detection

Chao Huang
Qianyi Li
Jie Wen
Bob Zhang

Pixel-level anomaly detection is indispensable in industrial defect detection and medical diagnosis. Recently, Segment Anything Model (SAM) has achieved promising results in many vision tasks. However, direct application of the SAM to pixel-level anomaly detection tasks results in unsatisfactory performance, meanwhile SAM needs the manual prompt. Although some automatically prompt-based SAM has been proposed, these automated prompting approaches merely utilize partial image features as prompts and fail to incorporate crucial features such as multi-scale image features to generate more suitable prompts. In this paper, we propose a novel Omni Dimensional State Space Model-driven SAM (ODS-SAM) for pixel-level anomaly detection. Specifically, the proposed method adopts the SAM architecture, ensuring easy implementation and avoiding the need for fine-tuning. A State-Space Model-based residual Omni Dimensional module is designed to automatically generate suitable prompts. This module can effectively leverage multi-scale and global information, facilitating an iterative search for optimal prompts in the prompt space. The identified optimal prompts are then fed into SAM as high-dimensional tensors. Experimental results demonstrate that the proposed ODS-SAM outperforms state-of-the-art models on both industrial and medical image datasets.

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IJCAI Conference 2025 Conference Paper

Towards VLM-based Hybrid Explainable Prompt Enhancement for Zero-Shot Industrial Anomaly Detection

Weichao Cai
Weiliang Huang
Yunkang Cao
Chao Huang
Fei Yuan
Bob Zhang
Jie Wen

Zero-Shot Industrial Anomaly Detection (ZSIAD) aims to identify and localize anomalies in industrial images from unseen categories. Owing to the powerful generalization capabilities, Vision-Language Models (VLMs) have achieved growing interest in ZSIAD. To guide the model toward understanding and localizing the semantically complex industrial anomalies, existing VLM-based methods have attempted to provide additional prompts to the model through learnable text prompt templates. However, these zero-shot methods lack detailed descriptions of specific anomalies, making it difficult to classify and segment the diverse range of industrial anomalies accurately. To address the aforementioned issue, we firstly propose the multi-stage prompt generation agent for ZSIAD. Specifically, we leverage the Multi-modal Language Large Model (MLLM) to articulate the detailed differential information between normal and test samples, which can provide detailed text prompts to the model through further refinement and anti-false alarm constraint. Moreover, we introduce the Visual Fundamental Model (VFM) to generate anomaly-related attention prompts for more accurate localization of anomalies with varying sizes and shapes. Extensive experiments on seven real-world industrial anomaly detection datasets have shown that the proposed method not only outperforms recent SOTA methods, but also its explainable prompts provide the model with a more intuitive basis for anomaly identification.

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IJCAI Conference 2024 Conference Paper

Implicit Prompt Learning for Image Denoising

Yao Lu
Bo Jiang
Guangming Lu
Bob Zhang

Recently, various deep denoising methods have been proposed to solve the insufficient feature problem in image denoising. These methods can be mainly classified into two categories: (1) Injecting learnable tensors into denoising backbone to supplement feature, which is effective to some extent but may cause serious over-fitting. (2) Using diverse natural images from large image datasets to synthesize noisy images and pre-train denoising models, which can bring model generalization but require large model size and expensive training costs. To address these issues, this paper proposes Implicit Prompt Learning for Image Denoising (IPLID) method to flexibly generate adaptive prompts without meticulously designing them. Specifically, we first introduce an efficient Linear Prompt (LP) block with ultra-few parameters to produce dynamic prompts for both different stages and samples in denoising procedure. We further propose an efficient Compact Feature Fusion (CFF) block to process previous multi-level prompted denoising feature to reconstruct the denoising images. Finally, to further efficiently and effectively produce satisfactory prompt and denoising performance, a Gradient Accumulation (GA) learning scheme is proposed. Experiments on multiple benchmarks showed that the proposed IPLID achieves competitive results with only 1 percent of pre-trained backbone parameters, outperforming classical denoising methods in both efficiency and quality of restored images.

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IJCAI Conference 2024 Conference Paper

QFormer: An Efficient Quaternion Transformer for Image Denoising

Bo Jiang
Yao Lu
Guangming Lu
Bob Zhang

Since Deep Convolutional Neural Networks (DCNNs) and Vision Transformer perform well in learning generalizable image priors from large-scale data, these models have been widely used in image denoising tasks. However, vanilla DCNNs and Transformer suffer from two problems. First, the vanilla DCNNs and Transformer only accumulate the output along the channel axis, ignoring the internal relationship among channels. This results in the severely inadequate color structure representation retrieved from color images. Secondly, the DCNNs or Transformer-based image denoising models usually have a large number of parameters, high computational complexity, and slow inference speed. To resolve these issues, this paper proposes a highly-efficient Quaternion Transformer (QFormer) for image denoising. Specifically, the proposed Quaternion Transformer Block (QTB) simplifies the typical Transformer from a multi-branch structure to an elaborately sequential structure mainly with quaternion transformations, to alternately capture both long-range dependencies and local contextual features with color structure information. Furthermore, the proposed QTB can also avoid considerable element-wise multiplications of computing the self-attention matrices. Thus, our QTB can significantly reduce the computational complexity and its sequential structure can further improve the practical inference speed. Comprehensive experiments demonstrate that the proposed QFormer produces state-of-the-art results in both denoising performance and efficiency. We hope that our work will encourage further research to explore the Quaternion Transformer architecture for image denoising tasks.

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TIST Journal 2023 Journal Article

Learning with Euler Collaborative Representation for Robust Pattern Analysis

Jianhang Zhou
Guancheng Wang
Shaoning Zeng
Bob Zhang

The Collaborative Representation (CR) framework has provided various effective and efficient solutions to pattern analysis. By leveraging between discriminative coefficient coding (l 2 regularization) and the best reconstruction quality (collaboration), the CR framework can exploit discriminative patterns efficiently in high-dimensional space. Due to the limitations of its linear representation mechanism, the CR must sacrifice its superior efficiency for capturing the non-linear information with the kernel trick. Besides this, even if the coding is indispensable, there is no mechanism designed to keep the CR free from inevitable noise brought by real-world information systems. In addition, the CR only emphasizes exploiting discriminative patterns on coefficients rather than on the reconstruction. To tackle the problems of primitive CR with a unified framework, in this article we propose the Euler Collaborative Representation (E-CR) framework. Inferred from the Euler formula, in the proposed method, we map the samples to a complex space to capture discriminative and non-linear information without the high-dimensional hidden kernel space. Based on the proposed E-CR framework, we form two specific classifiers: the Euler Collaborative Representation based Classifier (E-CRC) and the Euler Probabilistic Collaborative Representation based Classifier (E-PROCRC). Furthermore, we specifically designed a robust algorithm for E-CR (termed as R-E-CR ) to deal with the inevitable noises in real-world systems. Robust iterative algorithms have been specially designed for solving E-CRC and E-PROCRC. We correspondingly present a series of theoretical proofs to ensure the completeness of the theory for the proposed robust algorithms. We evaluated E-CR and R-E-CR with various experiments to show its competitive performance and efficiency.

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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.

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AAAI Conference 2022 Conference Paper

Generative Adaptive Convolutions for Real-World Noisy Image Denoising

Ruijun Ma
Shuyi Li
Bob Zhang
Zhengming Li

Recently, deep learning techniques are soaring and have shown dramatic improvements in real-world noisy image denoising. However, the statistics of real noise generally vary with different camera sensors and in-camera signal processing pipelines. This will induce problems of most deep denoisers for the overfitting or degrading performance due to the noise discrepancy between the training and test sets. To remedy this issue, we propose a novel flexible and adaptive denoising network, coined as FADNet. Our FADNet is equipped with a plane dynamic filter module, which generates weight filters with flexibility that can adapt to the specific input and thereby impedes the FADNet from overfitting to the training data. Specifically, we exploit the advantage of the spatial and channel attention, and utilize this to devise a decoupling filter generation scheme. The generated filters are conditioned on the input and collaboratively applied to the decoded features for representation capability enhancement. We additionally introduce the Fourier transform and its inverse to guide the predicted weight filters to adapt to the noisy input with respect to the image contents. Experimental results demonstrate the superior denoising performances of the proposed FADNet versus the state-of-the-art. In contrast to the existing deep denoisers, our FADNet is not only flexible and efficient, but also exhibits a compelling generalization capability, enjoying tremendous potential for practical usage.

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JBHI Journal 2021 Journal Article

Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis

Qi Zhang
Jianhang Zhou
Bob Zhang

It is well known in Traditional Chinese Medicine (TCM) that a person's wrist pulse signal can reflect their health condition. Recently, many computerized wrist pulse AI systems have been proposed to simulate a practitioner's three fingers in order to acquire the wrist pulse signals (three positions/channels) from a candidate's wrist dynamically, before evaluating their health status based on the various feature extraction and detection methods. However, few works have investigated the correlation of the extracted features from the three wrist channels and comprehensively fused the various features together, which can improve the performance of wrist pulse diagnosis. In this paper, we propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. In detail, two different sensors, i. e. , pressure and photoelectricity are used to capture the three channels of the wrist pulse signals. These are used to generate two different features by applying the stacked sparse autoencoder and wavelet scattering. Each feature of one wrist pulse sample is regarded as a node associated with its corresponding feature vector, and used to construct a graph for one candidate. A novel algorithm is implemented to construct different graphs for different candidates, which are used for wrist pulse diagnosis by developing graph convolutional networks. Experimental results indicate that our proposed AI-based method can obtain superior performances compared to other state-of-the-art approaches.

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AIIM Journal 2021 Journal Article

Multi-feature representation for burn depth classification via burn images

Bob Zhang
Jianhang Zhou

Burns are a common and severe problem in public health. Early and timely classification of burn depth is effective for patients to receive targeted treatment, which can save their lives. However, identifying burn depth from burn images requires physicians to have a lot of medical experience. The speed and precision to diagnose the depth of the burn image are not guaranteed due to its high workload and cost for clinicians. Thus, implementing some smart burn depth classification methods is desired at present. In this paper, we propose a computerized method to automatically evaluate the burn depth by using multiple features extracted from burn images. Specifically, color features, texture features and latent features are extracted from burn images, which are then concatenated together and fed to several classifiers, such as random forest to generate the burn level. A standard burn image dataset is evaluated by our proposed method, obtaining an Accuracy of 85. 86% and 76. 87% by classifying the burn images into two classes and three classes, respectively, outperforming conventional methods in the burn depth identification. The results indicate our approach is effective and has the potential to aid medical experts in identifying different burn depths.

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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.

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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.

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AAAI Conference 2019 Conference Paper

Super Sparse Convolutional Neural Networks

Yao Lu
Guangming Lu
Bob Zhang
Yuanrong Xu
Jinxing Li

To construct small mobile networks without performance loss and address the over-fitting issues caused by the less abundant training datasets, this paper proposes a novel super sparse convolutional (SSC) kernel, and its corresponding network is called SSC-Net. In a SSC kernel, every spatial kernel has only one non-zero parameter and these non-zero spatial positions are all different. The SSC kernel can effectively select the pixels from the feature maps according to its non-zero positions and perform on them. Therefore, SSC can preserve the general characteristics of the geometric and the channels’ differences, resulting in preserving the quality of the retrieved features and meeting the general accuracy requirements. Furthermore, SSC can be entirely implemented by the “shift” and “group point-wise” convolutional operations without any spatial kernels (e. g. , “3 × 3”). Therefore, SSC is the first method to remove the parameters’ redundancy from the both spatial extent and the channel extent, leading to largely decreasing the parameters and Flops as well as further reducing the img2col and col2img operations implemented by the low leveled libraries. Meanwhile, SSC-Net can improve the sparsity and overcome the over-fitting more effectively than the other mobile networks. Comparative experiments were performed on the less abundant CIFAR and low resolution ImageNet datasets. The results showed that the SSC-Nets can significantly decrease the parameters and the computational Flops without any performance losses. Additionally, it can also improve the ability of addressing the over-fitting problem on the more challenging less abundant datasets.

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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.

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AAAI Conference 2018 Conference Paper

A Probabilistic Hierarchical Model for Multi-View and Multi-Feature Classification

Jinxing Li
Hongwei Yong
Bob Zhang
Mu Li
Lei Zhang
David Zhang

Some recent works in classiﬁcation show that the data obtained from various views with different sensors for an object contributes to achieving a remarkable performance. Actually, in many real-world applications, each view often contains multiple features, which means that this type of data has a hierarchical structure, while most of existing works do not take these features with multi-layer structure into consideration simultaneously. In this paper, a probabilistic hierarchical model is proposed to address this issue and applied for classi- ﬁcation. In our model, a latent variable is ﬁrst learned to fuse the multiple features obtained from a same view, sensor or modality. Particularly, mapping matrices corresponding to a certain view are estimated to project the latent variable from a shared space to the multiple observations. Since this method is designed for the supervised purpose, we assume that the latent variables associated with different views are inﬂuenced by their ground-truth label. In order to effectively solve the proposed method, the Expectation-Maximization (EM) algorithm is applied to estimate the parameters and latent variables. Experimental results on the extensive synthetic and two real-world datasets substantiate the effectiveness and superiority of our approach as compared with state-of-the-art.

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IJCAI Conference 2018 Conference Paper

AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks

Yao Lu
Guangming Lu
Yuanrong Xu
Bob Zhang

In order to address the overfitting problem caused by the small or simple training datasets and the large model’s size in Convolutional Neural Networks (CNNs), a novel Auto Adaptive Regularization (AAR) method is proposed in this paper. The relevant networks can be called AAR-CNNs. AAR is the first method using the “abstraction extent” (predicted by AE net) and a tiny learnable module (SE net) to auto adaptively predict more accurate and individualized regularization information. The AAR module can be directly inserted into every stage of any popular networks and trained end to end to improve the networks’ flexibility. This method can not only regularize the network at both the forward and the backward processes in the training phase, but also regularize the network on a more refined level (channel or pixel level) depending on the abstraction extent’s form. Comparative experiments are performed on low resolution ImageNet, CIFAR and SVHN datasets. Experimental results show that the AAR-CNNs can achieve state-of-the-art performances on these datasets.

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