Journal of Visual Communication and Image Representation最新文献

{"title":"Dual-optimized two-stage Camouflaged Object Detection","authors":"Sanxin Jiang,&nbsp;Hongliang Zhang,&nbsp;Changde Ding","doi":"10.1016/j.jvcir.2025.104631","DOIUrl":"10.1016/j.jvcir.2025.104631","url":null,"abstract":"<div><div>To address the current issues of inaccurate object localization and insufficient edge information extraction in Camouflaged Object Detection (COD), inspired by how humans detect camouflaged objects—first identifying their general outline and then focusing on finer details—we propose a novel two-stage network, DONet, for COD. In the first stage, the network leverages an Edge Exploration Module (EEM) to locate object boundaries, refining this boundary information through Retrieve Attention. Subsequently, the Object Position Recognition Module (OPRM) detects the horizontal and vertical locations of camouflaged objects by integrating boundary information with high-level features. This information is further enhanced by combining multi-dilation channels and neighboring features. In the second stage, a Context Aggregation Module (CAM) is used to aggregate contextual information, improving detection accuracy. Extensive experiments demonstrate that DONet surpasses 16 state-of-the-art methods across three challenging datasets, highlighting its effectiveness and superior performance. In addition, DONet also has outstanding detection performance in the field of medical polyp segmentation.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104631"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring a Non-Parametric Uncertain Adaptive training method for facial expression recognition","authors":"Renhao Sun ,&nbsp;Chaoqun Wang ,&nbsp;Yujian Wang","doi":"10.1016/j.jvcir.2025.104636","DOIUrl":"10.1016/j.jvcir.2025.104636","url":null,"abstract":"<div><div>In facial expression recognition, the uncertainties impregnated by ambiguous facial expressions and subjectiveness of annotators lead to inter-class similarity and intra-class diversity among annotated samples, which in turn leads to deterioration of recognition results. To mitigate bad performance due to uncertainties, we explore a <strong>No</strong>n-<strong>P</strong>arametric <strong>U</strong>ncertain <strong>A</strong>daptive (NoPUA) method during the training process to suppress ambiguous samples for facial expression recognition. Specifically, we first propose a <em>self-paced feature bank module</em> on mini-batches to calculate the top-<em>K</em> similarity rank for each training sample, and then design a <em>sample-to-class weighting score module</em> based on the similarity rank to grade the different categories with respect to the similarity classes of the samples themselves. Finally, we modify the labels of each uncertain sample using the self-adaptive relabeling module for multi-category scoring described above. Our method is non-parametric and easy to implement. Moreover, it is model-agnostic. Extensive experiments on three public benchmarks (RAF-DB, FERPlus, AffectNet) validate the effectiveness of our NoPUA embedded in a variety of algorithms (baseline, SCN, RUL, EAC, DAN, POSTER++) and achieve better performance.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104636"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DSFusion: A dual-branch step-by-step fusion network for medical image fusion","authors":"Nili Tian,&nbsp;YuLong Ling,&nbsp;Qing Pan","doi":"10.1016/j.jvcir.2025.104700","DOIUrl":"10.1016/j.jvcir.2025.104700","url":null,"abstract":"<div><div>To address the limitations of insufficient consideration of multi-dimensional modal image features in existing medical image fusion methods, this paper proposes a novel dual-branch step-by-step fusion (DSFusion) network. The dual sequence extraction block (DSE) is used for initial feature extraction, followed by the multi-scale lightweight residual (MLRes) block for enhanced efficiency and generalization. Features are then fused through the global pixel-level multi-dimensional fusion (GPMF) module, comprising a multi-dimensional feature extraction block (MFEB) and a pixel-level global fusion branch (PGFB). Finally, fused features are reconstructed into the final image. Experiments performed on different modality datasets demonstrate that DSFusion achieves competitive or superior performance across multiple evaluation metrics in the four indicators as <span><math><msub><mrow><mi>Q</mi></mrow><mrow><mi>M</mi><mi>I</mi></mrow></msub></math></span>, <span><math><mrow><mi>P</mi><mi>S</mi><mi>N</mi><mi>R</mi></mrow></math></span>, and <span><math><msub><mrow><mi>Q</mi></mrow><mrow><mi>P</mi></mrow></msub></math></span> metrics.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104700"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PV3M-YOLO: A triple attention-enhanced model for detecting pedestrians and vehicles in UAV-enabled smart transport networks","authors":"Noor Ul Ain Tahir ,&nbsp;Li Kuang ,&nbsp;Melikamu Liyih Sinishaw ,&nbsp;Muhammad Asim","doi":"10.1016/j.jvcir.2025.104701","DOIUrl":"10.1016/j.jvcir.2025.104701","url":null,"abstract":"<div><div>Pedestrian and vehicle detection in aerial images remains a challenging task due to small object sizes, scale variation, and occlusion, often resulting in missed detections or the need for overly complex models. This study introduces the PV3M-YOLO approach, which incorporates three key focus modules to enhance the detection of small and undetectable objects. The proposed model integrates lightweight Ghost convolution, Convolutional Block Attention Module (CBAM), and Coordination Attention (CA) modules with optimized feature aggregation (C2f) into a unified architecture. These modifications improve the capacity of model to capture essential spatial details and contextual dependencies without increasing computational complexity. Furthermore, the Wise-IoUv3 loss calculus is employed to reduce the influence of subpar cases, enhancing localization and reducing erroneous identifications by reducing negative gradients. Experimental evaluations on VisDrone2019 dataset demonstrate that PV3M-YOLO achieves a [email protected] of 45.4% and [email protected]:95 of 27.9%, surpassing the baseline by 3.9% and 2.7% respectively. The model maintains efficiency with a compact size of 45.3 MB and a runtime of 8.8 ms. However, the detection of extremely small objects remains a limitation due to high-altitude nature of aerial imagery, indicating the need for future model enhancements targeting ultra-small object detection.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104701"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrared remote sensing small ship target detection method based on spatial-semantic enhancement and feature reconstruction neck","authors":"Fenglin Man ,&nbsp;Chaofeng Li ,&nbsp;Tuxin Guan","doi":"10.1016/j.jvcir.2025.104683","DOIUrl":"10.1016/j.jvcir.2025.104683","url":null,"abstract":"<div><div>This paper introduces an innovative infrared remote sensing small ship target detection network that integrates spatial-semantic enhancement with feature reconstruction neck. Spatial-semantic enhancement module is designed to selectively amplify diverse information within the backbone, effectively addressing the challenge of false alarms. It employs a parameter-free attention mechanism to improve the positional information of shallow feature maps and utilizes dynamic dilated convolution to capture contextual information from deep feature maps. Additionally, the feature reconstruction neck consolidates multi-scale feature maps across spatial and channel dimensions respectively, incorporating attention adjacent-layer concatenation to minimize missed detections. To further enhance detection capabilities, we have developed an infrared ship detection head that leverages the advantages of the DINO decoder while accounting for the size characteristics of infrared small targets. Experimental results from the public infrared ship detection dataset (ISDD) indicate that our approach surpasses some other state-of-the-art methods, demonstrating superior detection performance both qualitatively and quantitatively.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104683"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional decay attention for image shadow removal","authors":"Xiujin Zhu ,&nbsp;Chee-Onn Chow ,&nbsp;Joon Huang Chuah","doi":"10.1016/j.jvcir.2025.104694","DOIUrl":"10.1016/j.jvcir.2025.104694","url":null,"abstract":"<div><div>Existing Transformer-based shadow removal methods are limited by fixed window sizes, making it difficult to effectively model global information. In addition, they do not fully utilize the distance prior in shadow images. This study argues that shadow removal should model brightness variations between regions from a global perspective. Non-shadow areas near the shadow boundaries are the most important for restoring brightness in shadow regions, and their importance gradually decreases as the distance increases. To achieve this, a regional decay attention mechanism is proposed, which introduces a positional decay bias into the self-attention computation to enable dynamic modeling of contributions from different spatial positions. A local perception module is introduced to improve the model’s ability to capture local details, and a shadow removal model named FW-Former is developed. This model achieves superior performance across multiple datasets, demonstrates stable generalization capability, and maintains a low parameter count.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104694"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BIAN: Bidirectional interwoven attention network for retinal OCT image classification","authors":"Ahmed Alasri ,&nbsp;Zhixiang Chen ,&nbsp;Yalong Xiao ,&nbsp;Chengzhang Zhu ,&nbsp;Abdulrahman Noman ,&nbsp;Raeed Alsabri ,&nbsp;Harrison Xiao Bai","doi":"10.1016/j.jvcir.2025.104654","DOIUrl":"10.1016/j.jvcir.2025.104654","url":null,"abstract":"<div><div>Retinal diseases are a significant global health concern, requiring advanced diagnostic tools for early detection and treatment. Automated diagnosis of retinal diseases using deep learning can significantly enhance early detection and intervention efforts. However, conventional deep learning models that concentrate on localized perspectives often develop feature representations that lack sufficient semantic discriminative capability. Conversely, models that prioritize global semantic-level information may fail to capture essential, subtle local pathological features. To address this issue, we propose BIAN, a novel Bidirectional Interwoven Attention Network designed for the classification of retinal Optical Coherence Tomography (OCT) images. BIAN synergistically combines the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) by integrating a ResNet architecture backbone with a ViT backbone through a bidirectional interwoven attention block. This network enables the model to effectively capture both local features and global contextual information. Specifically, the bidirectional interwoven attention block allow the ResNet and ViT components to attend to each other’s feature representations, enhancing the network’s overall learning capacity. We evaluated BIAN on both the OCTID and OCTDL datasets for retinal disease classification. The OCTID dataset includes conditions such as Age-related Macular Degeneration (AMD), Macular Hole (MH), Central Serous Retinopathy (CSR), etc., while OCTDL covers AMD, Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Vein Occlusion (RVO), etc. On OCTID, the proposed model achieved 95.7% accuracy for five-class classification, outperforming existing state-of-the-art models. On OCTDL, BIAN attained 94.7% accuracy, with consistently high F1-scores (95.6% on OCTID, 94.6% on OCTDL) and AUC values (99.3% and 99.0%, respectively). These results highlight the potential of BIAN as a robust network for retinal OCT image classification in medical applications.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104654"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-view 3D model recognition via multi-label and multi-level fusion with bidirectional GRU","authors":"Daojie Zhou,&nbsp;Yihan Li,&nbsp;Yi Li","doi":"10.1016/j.jvcir.2025.104692","DOIUrl":"10.1016/j.jvcir.2025.104692","url":null,"abstract":"<div><div>This paper proposes a multi-view fusion-based recognition algorithm to address the imbalanced recognition accuracy of existing 3D model multi-view recognition methods. This imbalance arises from their failure to account for inter-view feature consistency and their use of inefficient fusion strategies. The proposed algorithm employs a dual-branch feature extraction network and a multi-label loss function to enforce the learning of consistent features across different views of the same model. Concurrently, a multi-level Gated Recurrent Unit (GRU) fusion network is constructed to efficiently integrate high-dimensional features from various levels and temporal information across multiple views. Simulation results demonstrate that the proposed algorithm achieves highly competitive recognition accuracy on mainstream benchmark datasets. Furthermore, it exhibits more balanced performance across different categories, thereby showing enhanced stability and robustness.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104692"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge distillation meets video foundation models: A video saliency prediction case study","authors":"Morteza Moradi ,&nbsp;Mohammad Moradi ,&nbsp;Concetto Spampinato ,&nbsp;Ali Borji ,&nbsp;Simone Palazzo","doi":"10.1016/j.jvcir.2026.104706","DOIUrl":"10.1016/j.jvcir.2026.104706","url":null,"abstract":"<div><div>Modeling spatio-temporal dynamics remains a major challenge and critical factor for effective video saliency prediction (VSP). The evolution from LSTM and 3D convolutional networks to vision transformers has sparked numerous innovations for tackling this complex video understanding task. However, current technologies still struggle to capture short- and long-term frame dependencies simultaneously. The emergence of large-scale video models has introduced unprecedented opportunities to overcome these limitations but poses significant practical challenges due to their substantial parameter counts and computational costs. To address this, we propose leveraging knowledge distillation—an approach yet to be fully explored in VSP solutions. Specifically, we employ THTD-Net, a leading transformer-based VSP architecture, as the student network, guided by a newly developed large-scale VSP model serving as the teacher. Evaluations on benchmark datasets confirm the efficacy of this novel approach, demonstrating promising performance and substantially reducing the complexity required for real-world applications.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104706"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iterative mutual voting matching for efficient and accurate Structure-from-Motion","authors":"Suning Ge ,&nbsp;Chunlin Ren ,&nbsp;Ya’nan He ,&nbsp;Linjie Li ,&nbsp;Jiaqi Yang ,&nbsp;Kun Sun ,&nbsp;Yanning Zhang","doi":"10.1016/j.jvcir.2025.104697","DOIUrl":"10.1016/j.jvcir.2025.104697","url":null,"abstract":"<div><div>As a crucial topic in 3D vision, Structure-from-Motion (SfM) aims to recover camera poses and 3D structures from unconstrained images. Performing pairwise image matching is a critical step. Typically, matching relationships are represented as a view graph, but the initial graph often contains redundant or potentially false edges, affecting both efficiency and accuracy. We propose an efficient incremental SfM method that optimizes the critical image matching step. Specifically, given an image similarity graph, an initialized weighted view graph is constructed. Next, the vertices and edges of the graph are treated as candidates and voters, with iterative mutual voting performed to score image pairs until convergence. Then, the optimal subgraph is extracted using the maximum spanning tree (MST). Finally, incremental reconstruction is carried out based on the selected images. We demonstrate the efficiency and accuracy of our method on general datasets and ambiguous datasets.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104697"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}