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Bin Wan

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5 papers

1 author row

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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EAAI Journal 2026 Journal Article

Feature boosting and scale-aware network with multi-modal information for underwater salient object detection

Tingyu Wang
Junzhe Lu
Bin Wan
Rongfeng Lu
Yaoqi Sun
Duanpo Wu
Yanbin Liu
Chenggang Yan

Underwater salient object detection (USOD) plays an important role in marine engineering applications. Due to the poor image quality caused by the complex underwater environment, USOD remains a challenging task. Existing methods typically fuse features extracted from underwater images and depth maps to exploit more salient cues. However, they neglect the degradation and noise corruption inherent in underwater multi-modal data. Moreover, these methods pay limited attention to the scale variations of underwater objects. To address these issues, we propose a feature boosting and scale-aware network (FBS-Net), which includes two processes: underwater feature boosting and scale-aware iterative fusion decoding. The underwater feature boosting process employs a scene contrast perception module (SCPM) to improve underwater Red–Green–Blue (RGB) image feature reliability through comparative analysis of the differences between RGB images and underwater enhanced images, and a frequency domain decoupling fusion module (FDFM) to reconstruct high-quality scene representations from noisy multi-modal inputs. In the scale-aware iterative fusion decoding process, a scale-aware iterative fusion decoder (SIFD) is introduced to dynamically process multi-scale information while suppressing noise and conflicting features through multiple refinement iterations. Extensive experiments on the USOD10K, COD10K, and USOD datasets demonstrate that our proposed method outperforms 16 state-of-the-art methods. Our code and results are available at https: //github. com/llllxxx2333/FBSNet.

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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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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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