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Xiaofei Zhou

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14 papers

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AAAI Conference 2026 Conference Paper

A Robust Unlearning Method with Adaptive Knowledge Guidance and Memory Preservation

Jingyuan Tian
Xiaofei Zhou

Machine unlearning has emerged as a promising approach to remove specific knowledge from large language models (LLMs), especially for safety-critical applications. However, existing representation-based methods lack guidance for selecting representation locations to unlearn (RMU), thus lacking precision in unlearning, while probability-based methods are vulnerable to fine-tuning attacks which use unrelated and safe data to fine-tune models. To address these problems, this paper presents an adaptive knowledge guidance and memory perturbation mechanisms, called ALMPU (Adaptive Localized Memory Perturbation Unlearning) which addresses the lack of knowledge guidance in representation-based unlearning methods and mitigates the impact of fine-tuning attacks on unlearned models. Specifically, we apply scaling factors to attention heads and select the most sensitive ones as knowledge guidance. Guided by the previous knowledge localization, we integrate enhanced memory perturbation—which forces the model to preserve specific knowledge—into the standard representation-based unlearning process at these sensitive positions. Through this perturbation mechanism, the model achieves more thorough elimination of the target knowledge. By adding interventions to selected attention heads and explicitly optimizing against fine-tuning attacks during the unlearning process, ALMPU creates a controlled divergence from the original model that is inherently resistant to relearning attempts. Experimental evaluation on the WMDP benchmark demonstrates that ALMPU consistently outperforms baseline methods across different scales of fine-tuning attacks.

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AAAI Conference 2026 Conference Paper

Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation

Runmin Cong
Anpeng Wang
Bin Wan
Cong Zhang
Xiaofei Zhou
Wei Zhang

Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains. To address this issue, we propose a Divide-and-Conquer Decoupled Network (DCDNet). In the training stage, to tackle feature entanglement that impedes cross-domain generalization and rapid adaptation, we propose the Adversarial-Contrastive Feature Decomposition (ACFD) module. It decouples backbone features into category-relevant private and domain-relevant shared representations via contrastive learning and adversarial learning. Then, to mitigate the potential degradation caused by the disentanglement, the Matrix-Guided Dynamic Fusion (MGDF) module adaptively integrates base, shared, and private features under spatial guidance, maintaining structural coherence. In addition, in the fine-tuning stage, to enhanced model generalization, the Cross-Adaptive Modulation (CAM) module is placed before the MGDF, where shared features guide private features via modulation ensuring effective integration of domain-relevant information. Extensive experiments on four challenging datasets show that DCDNet outperforms existing CD-FSS methods, setting a new state-of-the-art for cross-domain generalization and few-shot adaptation.

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AAAI Conference 2026 Conference Paper

SAM-DAQ: Segment Anything Model with Depth-guided Adaptive Queries for RGB-D Video Salient Object Detection

Jia Lin
Xiaofei Zhou
Jiyuan Liu
Runmin Cong
Guodao Zhang
Zhi Liu
Jiyong Zhang

Recently segment anything model (SAM) has attracted widespread concerns, and it is often treated as a vision foundation model for universal segmentation. Some researchers have attempted to directly apply the foundation model to the RGB-D video salient object detection (RGB-D VSOD) task, which often encounters three challenges, including the dependence on manual prompts, the high memory consumption of sequential adapters, and the computational burden of memory attention. To address the limitations, we propose a novel method, namely Segment Anything Model with Depth-guided Adaptive Queries (SAM-DAQ), which adapts SAM2 to pop-out salient objects from videos by seamlessly integrating depth and temporal cues within a unified framework. Firstly, we deploy a parallel adapter-based multi-modal image encoder (PAMIE), which incorporates several depth-guided parallel adapters (DPAs) in a skip-connection way. Remarkably, we fine-tune the frozen SAM encoder under prompt-free conditions, where the DPA utilizes depth cues to facilitate the fusion of multi-modal features. Secondly, we deploy a query-driven temporal memory (QTM) module, which unifies the memory bank and prompt embeddings into a learnable pipeline. Concretely, by leveraging both frame-level queries and video-level queries simultaneously, the QTM module can not only selectively extract temporal consistency features but also iteratively update the temporal representations of the queries. Extensive experiments are conducted on three RGB-D VSOD datasets, and the results show that the proposed SAM-DAQ consistently outperforms state-of-the-art methods in terms of all evaluation metrics.

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AIIM Journal 2025 Journal Article

Identifying signatures of image phenotypes to track treatment response in liver disease

Matthias Perkonigg
Nina Bastati
Ahmed Ba-Ssalamah
Peter Mesenbrink
Alexander Goehler
Miljen Martic
Xiaofei Zhou
Michael Trauner

Quantifiable image patterns associated with disease progression and treatment response are critical tools for guiding individual treatment, and for developing novel therapies. Here, we show that unsupervised machine learning can identify a pattern vocabulary of liver tissue in magnetic resonance images that quantifies treatment response in diffuse liver disease. Deep clustering networks simultaneously encode and cluster patches of medical images into a low-dimensional latent space to establish a tissue vocabulary. The resulting tissue types capture differential tissue change and its location in the liver associated with treatment response. We demonstrate the utility of the vocabulary in a randomized controlled trial cohort of patients with nonalcoholic steatohepatitis. First, we use the vocabulary to compare longitudinal liver change in a placebo and a treatment cohort. Results show that the method identifies specific liver tissue change pathways associated with treatment and enables a better separation between treatment groups than established non-imaging measures. Moreover, we show that the vocabulary can predict biopsy derived features from non-invasive imaging data. We validate the method in a separate replication cohort to demonstrate the applicability of the proposed method.

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EAAI Journal 2025 Journal Article

Multi-modal feature integration network for Visible-Depth-Thermal salient object detection

Fengyv Cui
Xiaofei Zhou
Liuxin Bao
Bin Wan
Ran Shi
Qiang Chen
Jiyong Zhang

In recent years, the task of salient object detection in multi-modal scenarios has attracted more and more attention, where the increase of modalities is beneficial for improving the detection performance of models. However, though the existing saliency models have achieved encouraging performance, they overlook the unbalanced information content between visible modality and other auxiliary modalities (i. e. , depth and thermal modalities), and lack the full utilization of multi-level features. This will lead to insufficient multi-modal fusion and multi-level integration. Therefore, in this paper, we propose a multi-modal feature integration network (MFINet) for Visible-Depth-Thermal (VDT) salient object detection (SOD), which contains three key modules. Firstly, we utilize the three-modal feature fusion (TMFF) module to enhance and fuse the multi-modal features by emphasizing effective feature channels and enlarging the receptive fields of features, where we further emphasize the visible cues. Secondly, we present a neighborhood layer feature enhancement (NLFE) module, which can utilize the complementary information from adjacent TMFF modules to enhance the decoder features by using different spatial attention strategies. Thirdly, a multi-level cascade feature integration (MCFI) module is proposed to aggregate the multi-level decoder features in a cascade way, acquiring the final high-quality saliency maps. Comprehensive experiments conducted on the VDT-2048 dataset demonstrate that our model outperforms the state-of-the-art models in terms of all evaluation metrics. The code is available at https: //github. com/banjamn/MFINet.

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EAAI Journal 2024 Journal Article

ADNet: Anti-noise dual-branch network for road defect detection

Bin Wan
Xiaofei Zhou
Yaoqi Sun
Tingyu Wang
Chengtao Lv
Shuai Wang
Haibing Yin
Chenggang Yan

This paper addresses the issue of noise interference in road defect detection, caused by various environmental factors or acquisition equipment. In this article, we add three different levels of salt & pepper noise to the road defect dataset and propose a novel anti-noise dual-branch network (ADNet). The proposed ADNet leverages two backbone networks equipped with the dual-branch interaction (DI) modules to learn the defect information from noise and clear images for improving noise immunity. Then, the weighted feature representation (WFR) module is designed to extract more context-aware cues from the multi-level feature. Additionally, the region perception unit is proposed, where channel-spatial attention optimization (CSAO) module extracts more defect region information by utilizing the attention mechanism and multi-scale refinement (MR) optimizes the boundary information with the U-Net structure. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art methods, making it a promising solution for detecting road defects in noisy environments.

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AAAI Conference 2024 Conference Paper

Quad Bayer Joint Demosaicing and Denoising Based on Dual Encoder Network with Joint Residual Learning

Bolun Zheng
Haoran Li
Quan Chen
Tingyu Wang
Xiaofei Zhou
Zhenghui Hu
Chenggang Yan

The recent imaging technology Quad Bayer CFA brings better imaging PSNR and higher visual quality compared to traditional Bayer CFA, but also serious challenges for demosaicing and denoising during the ISP pipeline. In this paper, we propose a novel dual encoder network, namely DRNet, to achieve joint demosaicing and denoising for Quad Bayer CFA. The dual encoders are carefully designed in that one is mainly constructed by a joint residual block to jointly estimate the residuals for demosaicing and denoising separately. In contrast, the other one is started with a pixel modulation block which is specially designed to match the characteristics of Quad Bayer pattern for better feature extraction. We demonstrate the effectiveness of each proposed component through detailed ablation investigations. The comparison results on public benchmarks illustrate that our DRNet achieves an apparent performance gain~(0.38dB to the 2nd best) from the state-of-the-art method and balances performance and efficiency well. The experiments on real-world images show that the proposed method could enhance the reconstruction quality from the native ISP algorithm.

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EAAI Journal 2023 Journal Article

SMINet:Semantics-aware multi-level feature interaction network for surface defect detection

Bin Wan
Xiaofei Zhou
Yaoqi Sun
Zunjie Zhu
Haibing Yin
Ji Hu
Jiyong Zhang
Chenggang Yan

To boost the product quality, numerous saliency-based surface defect detection methods have been devoted to the areas of industrial production, construction consumable, road construction. However, the existing salient object detection (SOD) methods not only consume a significant amount of computing resources but also fail to meet the detection efficiency requirements of enterprises. Therefore, this paper proposes a lightweight semantics-aware multi-level feature interaction network (SMINet), to address the above issues. In the encoder phase, we integrate multiple adjacent level features in the cross-layer feature fusion (CFF) module to alleviate the discrepancy between multi-scale features. In the decoder phase, we first employ the semantic-aware feature extraction (SFE) module to mine the location cues embedded in the high-level features. Afterwards, we introduce the detail-aware context attention (DCA) module based on the attention mechanism to recover more spatial details. Extensive experiments on four surface defect datasets validate that our SMINet outperforms the existing state-of-the-art methods.

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AAAI Conference 2022 Conference Paper

Exploring Relational Semantics for Inductive Knowledge Graph Completion

Changjian Wang
Xiaofei Zhou
Shirui Pan
Linhua Dong
Zeliang Song
Ying Sha

Knowledge graph completion (KGC) aims to infer missing information in incomplete knowledge graphs (KGs). Most previous works only consider the transductive scenario where entities are existing in KGs, which cannot work effectively for the inductive scenario containing emerging entities. Recently some graph neural network-based methods have been proposed for inductive KGC by aggregating neighborhood information to capture some uncertainty semantics from the neighboring auxiliary triples. But these methods ignore the more general relational semantics underlying all the known triples that can provide richer information to represent emerging entities so as to satisfy the inductive scenario. In this paper, we propose a novel model called CFAG, which utilizes two granularity levels of relational semantics in a coarsegrained aggregator (CG-AGG) and a fine-grained generative adversarial net (FG-GAN), for inductive KGC. The CG-AGG firstly generates entity representations with multiple semantics through a hypergraph neural network-based global aggregator and a graph neural network-based local aggregator, and the FG-GAN further enhances entity representations with specific semantics through conditional generative adversarial nets. Experimental results on benchmark datasets show that our model outperforms state-of-the-art models for inductive KGC.

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AAAI Conference 2021 Conference Paper

Image Captioning with Context-Aware Auxiliary Guidance

Zeliang Song
Xiaofei Zhou
Zhendong Mao
Jianlong Tan

Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image. Most recent researches follow the encoder-decoder framework which depends heavily on the previous generated words for the current prediction. Such methods can not effectively take advantage of the future predicted information to learn complete semantics. In this paper, we propose Context-Aware Auxiliary Guidance (CAAG) mechanism that can guide the captioning model to perceive global contexts. Upon the captioning model, CAAG performs semantic attention that selectively concentrates on useful information of the global predictions to reproduce the current generation. To validate the adaptability of the method, we apply CAAG to three popular captioners and our proposal achieves competitive performance on the challenging Microsoft COCO image captioning benchmark, e. g. 132. 2 CIDEr-D score on Karpathy split and 130. 7 CIDEr-D (c40) score on official online evaluation server.

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AAAI Conference 2020 Conference Paper

A Knowledge-Aware Attentional Reasoning Network for Recommendation

Qiannan Zhu
Xiaofei Zhou
Jia Wu
Jianlong Tan
Li Guo

Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently. Many existing knowledge-aware recommendation methods have achieved better performance, which usually perform recommendation by reasoning on the paths between users and items in knowledge graphs. However, they ignore the users’ personal clicked history sequences that can better reﬂect users’ preferences within a period of time for recommendation. In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users’ clicked history sequences and path connectivity between users and items for recommendation. The proposed KARN not only develops an attention-based RNN to capture the user’s history interests from the user’s clicked history sequences, but also a hierarchical attentional neural network to reason on paths between users and items for inferring the potential user intents on items. Based on both user’s history interest and potential intent, KARN can predict the clicking probability of the user with respective to a candidate item. We conduct experiment on Amazon review dataset, and the experimental results demonstrate the superiority and effectiveness of our proposed KARN model.

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IJCAI Conference 2020 Conference Paper

A Relation-Specific Attention Network for Joint Entity and Relation Extraction

Yue Yuan
Xiaofei Zhou
Shirui Pan
Qiannan Zhu
Zeliang Song
Li Guo

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recognition followed by relation detection between every possible entity pairs, which usually suffers from numerous redundant operations. In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. Our RSAN utilizes relation-aware attention mechanism to construct specific sentence representations for each relation, and then performs sequence labeling to extract its corresponding head and tail entities. Experiments on two public datasets show that our model can effectively extract overlapping triplets and achieve state-of-the-art performance.

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AAAI Conference 2019 Conference Paper

DAN: Deep Attention Neural Network for News Recommendation

Qiannan Zhu
Xiaofei Zhou
Zeliang Song
Jianlong Tan
Li Guo

With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing richer hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.

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IJCAI Conference 2019 Conference Paper

Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs

Qiannan Zhu
Xiaofei Zhou
Jia Wu
Jianlong Tan
Li Guo

Multilingual knowledge graphs constructed by entity alignment are the indispensable resources for numerous AI-related applications. Most existing entity alignment methods only use the triplet-based knowledge to find the aligned entities across multilingual knowledge graphs, they usually ignore the neighborhood subgraph knowledge of entities that implies more richer alignment information for aligning entities. In this paper, we incorporate neighborhood subgraph-level information of entities, and propose a neighborhood-aware attentional representation method NAEA for multilingual knowledge graphs. NAEA devises an attention mechanism to learn neighbor-level representation by aggregating neighbors' representations with a weighted combination. The attention mechanism enables entities not only capture different impacts of their neighbors on themselves, but also attend over their neighbors' feature representations with different importance. We evaluate our model on two real-world datasets DBP15K and DWY100K, and the experimental results show that the proposed model NAEA significantly and consistently outperforms state-of-the-art entity alignment models.

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