Journal of Visual Communication and Image Representation最新文献

{"title":"MemFlow-AD: An anomaly detection and localization model based on memory module and normalizing flow","authors":"Xin Xie,&nbsp;Zixi Li,&nbsp;Shenping Xiong,&nbsp;Zhaoyang Liu,&nbsp;Tijian Cai","doi":"10.1016/j.jvcir.2025.104454","DOIUrl":"10.1016/j.jvcir.2025.104454","url":null,"abstract":"<div><div>Presently, in most anomaly detection methods, the training dataset contains a low frequency of anomalous data with diverse categories. However, these methods exhibit limited learning capacity for anomalous information, leading to weak model generalization and low detection accuracy. This paper proposes MemFlow-AD, an anomaly detection and localization model that integrates a memory module and a normalizing flow. MemFlow-AD supplements anomalous data using anomaly simulation, retains general patterns from normal samples via the memory module to discern potential differences between normal and anomalous samples, employs a 2D normalizing flow to extract distributional feature information from the data, and through multiscale feature fusion and attention mechanism to further enhance the feature expression ability of the model. Experimental results demonstrate outstanding performance in detecting and localizing anomalies on the MVTec dataset, achieving accuracies of 98.61% and 94.02%, respectively. Moreover, on the BTAD dataset, the model exhibits a 2.15% improvement in detection accuracy compared to current mainstream methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104454"},"PeriodicalIF":2.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815162","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":"MES-YOLO: An efficient lightweight maritime search and rescue object detection algorithm with improved feature fusion pyramid network","authors":"Zhao Jin ,&nbsp;Tian He ,&nbsp;Liping Qiao ,&nbsp;Jiang Duan ,&nbsp;Xinyu Shi ,&nbsp;Bohan Yan ,&nbsp;Chen Guo","doi":"10.1016/j.jvcir.2025.104453","DOIUrl":"10.1016/j.jvcir.2025.104453","url":null,"abstract":"<div><div>Maritime Search and Rescue (SAR) object detection is challenged by environmental complexity, variability in object scales, and real-time computation constraints of Unmanned Aerial Vehicles (UAVs). Our MES-YOLO algorithm, designed for maritime UAV imagery, employs an innovative Multi Asymptotic Feature Pyramid Network (MAFPN) to enhance detection accuracy across scales. It integrates an Efficient Module (EMO) and Inverted Residual Mobile Blocks (iRMB) to maintain a lightweight model while enhancing key information perception.The SIoU loss function is used to optimize the detection performance of the model. Tests on the SeaDronesSee dataset show that MES-YOLO increased average precision (mAP50) from 81.5% to 87.1%, reduced parameter count by 43.3%, and improved the F1 score by 6.8%, with a model size only 58.3% that of YOLOv8, surpassing YOLO series and other mainstream algorithms in robustness to background illumination and imaging angles.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104453"},"PeriodicalIF":2.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808707","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":"Reversible data hiding with automatic contrast enhancement and high embedding capacity based on multi-type histogram modification","authors":"Libo Han ,&nbsp;Wanlin Gao ,&nbsp;Xinfeng Zhang ,&nbsp;Sha Tao","doi":"10.1016/j.jvcir.2025.104450","DOIUrl":"10.1016/j.jvcir.2025.104450","url":null,"abstract":"<div><div>For an image, we can use reversible data hiding (RDH) with automatic contrast enhancement (ACE) to automatically improve its contrast by continuously embedding data. Some existing methods may make the detailed information in the dark regions of the grayscale image not well presented. Furthermore, these methods sometimes suffer from low embedding capacity (EC). Therefore, we propose an RDH method with ACE and high EC based on multi-type histogram modification. A pixel value histogram modification method is proposed to improve the contrast automatically. In this method, two-sided histogram expansion is used to improve global contrast, and then the histogram right-shift method is used to enhance the dark regions. Then, a prediction error histogram modification method is proposed to improve the EC. In this method, a new prediction method is proposed to better improve the EC. Experiment results show that compared with some advanced methods, the proposed method performs better.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104450"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768971","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-level cross-modal attention guided DIBR 3D image watermarking","authors":"Qingmo Chen ,&nbsp;Zhang Wang ,&nbsp;Zhouyan He ,&nbsp;Ting Luo ,&nbsp;Jiangtao Huang","doi":"10.1016/j.jvcir.2025.104455","DOIUrl":"10.1016/j.jvcir.2025.104455","url":null,"abstract":"<div><div>For depth-image-based rendering (DIBR) 3D images, both center and synthesized virtual views are subject to illegal distribution during transmission. To address the issue of copyright protection of DIBR 3D images, we propose a multi-level cross-modal attention guided network (MCANet) for 3D image watermarking. To optimize the watermark embedding process, the watermark adjustment module (WAM) is designed to extract cross-modal information at different scales, thereby calculating 3D image attention to adjust the watermark distribution. Furthermore, the nested dual output U-net (NDOU) is devised to enhance the compensatory capability of the skip connections, thus providing an effective global feature to the up-sampling process for high image quality. Compared to state-of-the-art (SOTA) 3D image watermarking methods, the proposed watermarking model shows superior performance in terms of robustness and imperceptibility.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104455"},"PeriodicalIF":2.6,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LDINet: Latent decomposition-interpolation for single image fast-moving objects deblatting","authors":"Haodong Fan,&nbsp;Dingyi Zhang,&nbsp;Yunlong Yu,&nbsp;Yingming Li","doi":"10.1016/j.jvcir.2025.104439","DOIUrl":"10.1016/j.jvcir.2025.104439","url":null,"abstract":"<div><div>The image of fast-moving objects (FMOs) usually contains a blur stripe indicating the blurred object that is mixed with the background. In this work we propose a novel Latent Decomposition-Interpolation Network (LDINet) to generate the appearances and shapes of the objects from the blurry stripe contained in the single image. In particular, we introduce an Decomposition-Interpolation Module (DIM) to break down the feature maps of the inputs into discrete time indexed parts and interpolate the target latent frames according to the provided time indexes with affine transformations, where the features are categorized into the scalar-like and gradient-like parts when warping in the interpolation. Finally, a decoder renders the prediction results. In addition, based on the results, a Refining Conditional Deblatting (RCD) approach is presented to further enhance the fidelity. Extensive experiments are conducted and have shown that the proposed methods achieve superior performances compared to the existing competing methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104439"},"PeriodicalIF":2.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725251","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":"VDD: Varied Drone Dataset for semantic segmentation","authors":"Wenxiao Cai,&nbsp;Ke Jin,&nbsp;Jinyan Hou,&nbsp;Cong Guo,&nbsp;Letian Wu,&nbsp;Wankou Yang","doi":"10.1016/j.jvcir.2025.104429","DOIUrl":"10.1016/j.jvcir.2025.104429","url":null,"abstract":"<div><div>Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing. While existing datasets typically focus on urban scenes and are relatively small, our Varied Drone Dataset (VDD) addresses these limitations by offering a large-scale, densely labeled collection of 400 high-resolution images spanning 7 classes. This dataset features various scenes in urban, industrial, rural, and natural areas, captured from different camera angles and under diverse lighting conditions. We also make new annotations to UDD (Chen et al., 2018) and UAVid (Lyu et al., 2018), integrating them under VDD annotation standards, to create the Integrated Drone Dataset (IDD). We train seven state-of-the-art models on drone datasets as baselines. It is expected that our dataset will generate considerable interest in drone image segmentation and serve as a foundation for other drone vision tasks. Datasets are publicly available at <span><span>https://github.com/RussRobin/VDD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104429"},"PeriodicalIF":2.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739111","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":"RBMark: Robust and blind video watermark in DT CWT domain","authors":"I.-Chun Huang ,&nbsp;Ji-Yan Wu ,&nbsp;Wei Tsang Ooi","doi":"10.1016/j.jvcir.2025.104438","DOIUrl":"10.1016/j.jvcir.2025.104438","url":null,"abstract":"<div><div>Video watermark embedding algorithms based on the transform domain are robust against media processing methods but are limited in data embedding capacity. Learning-based watermarking algorithms have recently become increasingly popular because of their good performance in image feature extraction and data embedding. However, they are not consistently robust against various video processing methods. To effectively trade off the embedding capacity and robustness, this paper proposes RBMark, a novel video watermarking method based on Dual-Tree Complex Wavelet Transform (DT CWT). First, the watermark bit-stream is transformed into a key image in the embedding phase. Second, we extract the DT CWT domain coefficients from both the video frame and the key image and embed the key image coefficients into the video frame coefficients. During the extraction phase, the key image coefficients are extracted to perform inverse DT CWT and reconstruct the watermark bit-stream. Compared with prior watermarking algorithms, RBMark achieves higher robustness and embedding capacity. We evaluated its performance using multiple representative video datasets to compare with prior transform domain and learning-based watermarking algorithms. Experimental results demonstrate that RBMark achieves up to 98% and 99% improvement in Bit Error Rate over the transform domain and learning-based methods, respectively. Furthermore, RBMark can embed at most 2040 bits in each 1080p-resolution video frame (i.e., <span><math><mrow><mn>9</mn><mo>.</mo><mn>84</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math></span> bits per pixel). The source code is available in <span><span>this URL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104438"},"PeriodicalIF":2.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705134","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":"Improving model generalization by on-manifold adversarial augmentation in the frequency domain","authors":"Chang Liu ,&nbsp;Wenzhao Xiang ,&nbsp;Yuan He ,&nbsp;Hui Xue ,&nbsp;Shibao Zheng ,&nbsp;Hang Su","doi":"10.1016/j.jvcir.2025.104437","DOIUrl":"10.1016/j.jvcir.2025.104437","url":null,"abstract":"<div><div>Deep Neural Networks (DNNs) often suffer from performance drops when training and test data distributions differ. Ensuring model generalization for Out-Of-Distribution (OOD) data is crucial, but current models still struggle with accuracy on such data. Recent studies have shown that regular or off-manifold adversarial examples as data augmentation improve OOD generalization. Building on this, we provide theoretical validation that on-manifold adversarial examples can enhance OOD generalization even more. However, generating these examples is challenging due to the complexity of real manifolds. To address this, we propose AdvWavAug, an on-manifold adversarial data augmentation method using a Wavelet module. This approach, based on the AdvProp training framework, leverages wavelet transformation to project an image into the wavelet domain and modifies it within the estimated data manifold. Experiments on various models and datasets, including ImageNet and its distorted versions, show that our method significantly improves model generalization, especially for OOD data.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104437"},"PeriodicalIF":2.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686193","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-TuneV: Fine-tuning the fusion of multiple modules for video action recognition","authors":"Xinyuan Liu,&nbsp;Junyong Ye,&nbsp;Jingjing Wang,&nbsp;Guangyi Xu,&nbsp;Youwei Li,&nbsp;Chaoming Zheng","doi":"10.1016/j.jvcir.2025.104441","DOIUrl":"10.1016/j.jvcir.2025.104441","url":null,"abstract":"<div><div>The current pre-trained models have achieved remarkable success, but they usually have complex structures and hundreds of millions of parameters, resulting in a huge computational resource requirement to train or fully fine-tune a pre-trained model, which limits its transfer learning on different tasks. In order to migrate pre-trained models to the field of Video Action Recognition (VAR), recent research uses parametric efficient transfer learning (PETL) approaches, while most of them are studied on a single fine-tuning module. For a complex task like VAR, a single fine-tuning method may not achieve optimal results. To address this challenge, we want to study the effect of joint fine-tuning with multiple modules, so we propose a method that merges multiple fine-tuning modules, namely Multi-TuneV. It combines five fine-tuning methods, including ST-Adapter, AdaptFormer, BitFit, VPT and LoRA. We design a particular architecture for Multi-TuneV and integrate it organically into the Video ViT model so that it can coordinate the multiple fine-tuning modules to extract features. Multi-TuneV enables pre-trained models to migrate to video classification tasks while maintaining improved accuracy and effectively limiting the number of tunable parameters, because it combines the advantages of five fine-tuning methods. We conduct extensive experiments with Multi-TuneV on three common video datasets, and show that it surpasses both full fine-tuning and other single fine-tuning methods. When only 18.7 % (16.09 M) of the full fine-tuning parameters are updated, the accuracy of Multi-TuneV on SSv2 and HMDB51 improve by 23.43 % and 16.46 % compared with the full fine-tuning strategy, and improve to 67.43 % and 75.84 %. This proves the effectiveness of joint multi-module fine-tuning. Multi-TuneV provides a new idea for PETL and a new perspective to address the challenge in video understanding tasks. Code is available at <span><span>https://github.com/hhh123-1/Multi-TuneV</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104441"},"PeriodicalIF":2.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686194","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":"DBFAM: A dual-branch network with efficient feature fusion and attention-enhanced gating for medical image segmentation","authors":"Benzhe Ren ,&nbsp;Yuhui Zheng ,&nbsp;Zhaohui Zheng ,&nbsp;Jin Ding ,&nbsp;Tao Wang","doi":"10.1016/j.jvcir.2025.104434","DOIUrl":"10.1016/j.jvcir.2025.104434","url":null,"abstract":"<div><div>In the field of medical image segmentation, convolutional neural networks (CNNs) and transformer networks have garnered significant attention due to their unique advantages. However, CNNs have limitations in modeling long-range dependencies, while transformers are constrained by their quadratic computational complexity. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. These models excel in capturing long-range interactions while maintaining linear computational complexity. This paper proposes a dual-branch parallel network that combines CNNs with Visual State Space Models (VSSMs). The two branches of the encoder separately capture local and global information. To further leverage the intricate relationships between local and global features, a dual-branch local–global feature fusion module is introduced, effectively integrating features from both branches. Additionally, an Attention-Enhanced Gated Module is proposed to replace traditional skip connections, aiming to improve the alignment of information transfer between the encoder and decoder. Extensive experiments on multiple datasets validate the effectiveness of our method.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"109 ","pages":"Article 104434"},"PeriodicalIF":2.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686192","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}