Journal of Visual Communication and Image Representation最新文献_第7页

Point cloud accumulation via multi-dimensional pseudo label and progressive instance association 基于多维伪标签和渐进实例关联的点云积累

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-16 DOI: 10.1016/j.jvcir.2025.104688

Shujuan Huang, Jie Pan, Chunyu Lin, Lang Nie, Meiqin Liu, Yao Zhao

{"title":"Point cloud accumulation via multi-dimensional pseudo label and progressive instance association","authors":"Shujuan Huang, Jie Pan, Chunyu Lin, Lang Nie, Meiqin Liu, Yao Zhao","doi":"10.1016/j.jvcir.2025.104688","DOIUrl":"10.1016/j.jvcir.2025.104688","url":null,"abstract":"<div><div>Point cloud accumulation aligns and merges 3D LiDAR frames to create dense, comprehensive scene representations that are critical for applications like autonomous driving. Effective accumulation relies on accurate scene flow estimation, yet error propagation and drift, particularly from noise and fast-moving objects, pose significant challenges. Existing clustering-based methods for instance association often falter under these conditions and depend heavily on manual labels, limiting scalability. To address these issues, we propose a Progressive Instance Association (PIA) method that integrates single-frame clustering with an enhanced Unscented Kalman Filter, improving tracking robustness in dynamic scenes. Additionally, our Multi-Dimensional Pseudo Label (MDPL) strategy leverages cross-modal supervision to reduce reliance on manual labels, enhancing scene flow accuracy. Evaluated on the Waymo Open Dataset, our method surpasses state-of-the-art LiDAR-based approaches and performs comparably to multi-modal methods. Qualitative visualizations further demonstrate denser, well-aligned accumulated point clouds.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104688"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797413","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}

引用次数: 0

Depth error points optimization for 3D Gaussian Splatting in few-shot synthesis 少镜头合成中三维高斯溅射深度误差点优化

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-16 DOI: 10.1016/j.jvcir.2025.104682

Xu Jiang , Huiping Deng , Sen Xiang , Li Yu

{"title":"Depth error points optimization for 3D Gaussian Splatting in few-shot synthesis","authors":"Xu Jiang , Huiping Deng , Sen Xiang , Li Yu","doi":"10.1016/j.jvcir.2025.104682","DOIUrl":"10.1016/j.jvcir.2025.104682","url":null,"abstract":"<div><div>Few-view 3D reconstruction technology aims to recover the 3D geometric shape of objects or scenes using only a limited number of views. In recent years, with the development of deep learning and 3D rendering technologies, this field has achieved significant progress. Due to the highly similar geometric and appearance features of repetitive texture regions, current few-view 3D reconstruction methods fail to distinguish their local differences during the global reconstruction process, thus frequently resulting in floating artifacts in these regions of the synthesized new views. We propose a method combining monocular depth supervision with depth-error-guided point optimization within the framework of 3D Gaussian Splatting to solve the floating artifact problem in repetitive texture regions under few-view input conditions. Specifically, we calculate a loss function using rendered depth maps and pseudo-true depth maps to achieve depth constraints, and we identify erroneous Gaussian points through depth error maps. For these erroneous point regions, we implement more effective point densification to guide the model in learning more correct geometric shapes in these regions and to synthesize views with fewer floating artifacts. We validate our method on the NeRF-LLFF dataset with different numbers of images. We conduct multiple experiments on randomly selected training images and provide average values to ensure fairness. The experimental results on the LLFF dataset show that our method outperforms the baseline method DRGS, achieving 0.53 dB higher PSNR and 0.021 higher SSIM. This confirms that we effectively reduce floating artifacts in the repetitive texture regions of few-view novel view synthesis.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104682"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797416","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}

引用次数: 0

Effective face recognition from video using enhanced social collie optimization-based deep convolutional neural network technique 基于增强社会牧羊犬优化的深度卷积神经网络技术有效地识别视频中的人脸

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-06 DOI: 10.1016/j.jvcir.2025.104639

Jitendra Chandrakant Musale , Anuj kumar Singh , Swati Shirke

引用次数: 0

Deep semi-supervised learning method based on sample adaptive weights and discriminative feature learning 基于样本自适应权值和判别特征学习的深度半监督学习方法

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-15 DOI: 10.1016/j.jvcir.2025.104689

Jiawei Wang, Weiwei Shi, Xiaofan Wang, Xinhong Hei

{"title":"Deep semi-supervised learning method based on sample adaptive weights and discriminative feature learning","authors":"Jiawei Wang, Weiwei Shi, Xiaofan Wang, Xinhong Hei","doi":"10.1016/j.jvcir.2025.104689","DOIUrl":"10.1016/j.jvcir.2025.104689","url":null,"abstract":"<div><div>Semi-supervised learning has achieved significant success through various approaches based on pseudo-labeling and consistency regularization. Despite efforts, effectively utilizing both labeled and unlabeled data remains a significant challenge. In this study, to enhance the efficient utilization of limited and valuable labeled data, we propose a self-adaptive weight redistribution strategy within a batch. This operation takes into account the heterogeneity of labeled data, adjusting its contribution to the overall loss based on sample-specific losses. This enables the model to more accurately identify challenging samples. Our experiments demonstrate that this weight reallocation strategy significantly enhances the model’s generalization ability. Additionally, to enhance intra-class compactness and inter-class separation of the learned features, we introduce a cosine similarity-based discriminative feature learning regularization term. This regularization term aims to reinforce feature consistency within the same class and enhance feature distinctiveness across different classes. Through this mechanism, we facilitate the model to prioritize learning discriminative feature representations, ensuring that features with authentic labels and those with high-confidence pseudo-labels are grouped together, while simultaneously separating features belonging to different clusters. The method can be combined with mainstream Semi-supervised learning methods, which we evaluate experimentally. Our experimental findings illustrate the efficacy of our approach in enhancing the performance of semi-supervised learning tasks across widely utilized image classification datasets.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104689"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797417","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}

引用次数: 0

An optical remote sensing ship detection model based on feature diffusion and higher-order relationship modeling 基于特征扩散和高阶关系建模的光学遥感舰船检测模型

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2026-01-04 DOI: 10.1016/j.jvcir.2025.104695

Chunman Yan, Ningning Qi

{"title":"An optical remote sensing ship detection model based on feature diffusion and higher-order relationship modeling","authors":"Chunman Yan, Ningning Qi","doi":"10.1016/j.jvcir.2025.104695","DOIUrl":"10.1016/j.jvcir.2025.104695","url":null,"abstract":"<div><div>Ship detection plays an increasingly important role in the field of marine monitoring, with Optical Remote Sensing (ORS) technology providing high-resolution spatial and texture information support. However, existing ship detection methods still face significant challenges in accurately detecting small targets, suppressing complex background interference, and modeling cross-scale semantic relationships, limiting their effectiveness in practical applications. Inspired by feature diffusion theory and higher-order spatial interaction mechanisms, this paper proposes a ship detection model for Optical Remote Sensing imagery. Specifically, to address the problem of fine-grained information loss during feature downsampling, the Single-branch and Dual-branch Residual Feature Downsampling (SRFD and DRFD) modules are designed to enhance small target preservation and multi-scale robustness. To capture long-range spatial dependencies and improve robustness against target rotation variations, the Fast Spatial Pyramid Pooling module based on Large Kernel Separable Convolution Attention (SPPF-LSKA) is introduced, enabling efficient large receptive field modeling with rotation-invariant constraints. Furthermore, to dynamically model complex semantic dependencies between different feature scales, the Feature Diffusion Pyramid Network (FDPN) is proposed based on continuous feature diffusion and cross-scale graph reasoning. Experimental results show that model achieves an <em>AP<sub>50</sub></em> of 86.2 % and an <em>AP<sub>50-95</sub></em> of 58.0 % on multiple remote sensing ship detection datasets, with the number of parameters reduced to 2.6 M and the model size compressed to 5.5 MB, significantly outperforming several state-of-the-art models in terms of both detection accuracy and lightweight deployment. These results demonstrate the detection capability, robustness, and application potential of the proposed model in Optical Remote Sensing ship monitoring tasks.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104695"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925010","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}

引用次数: 0

Corrigendum to “Lightweight macro-pixel quality enhancement network for light field images compressed by versatile video coding” [J. Vis. Commun. Image Represent. 105 (2024) 104329] “多用途视频编码压缩光场图像的轻量级宏像素质量增强网络”[J]。粘度Commun。图像代表。105 (2024)104329]

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jvcir.2025.104673

Hongyue Huang , Chen Cui , Chuanmin Jia , Xinfeng Zhang , Siwei Ma

引用次数: 0

Vision-language tracking with attention-based optimization 基于注意力优化的视觉语言跟踪

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-15 DOI: 10.1016/j.jvcir.2025.104644

Shuo Hu , Tongtong Liu , Liyang Han , Run Xing

{"title":"Vision-language tracking with attention-based optimization","authors":"Shuo Hu , Tongtong Liu , Liyang Han , Run Xing","doi":"10.1016/j.jvcir.2025.104644","DOIUrl":"10.1016/j.jvcir.2025.104644","url":null,"abstract":"<div><div>Most existing visual tracking methods typically employ image patches as target references and endeavor to enhance tracking performance by maximizing the utilization of visual information through various deep networks. However, due to the intrinsic limitations of visual information, the performance of the trackers significantly deteriorates when confronted with drastic target variations or complex background environments. To address these issues, we propose a vision-language multimodal fusion tracker for object tracking. Firstly, we use semantic information from language descriptions to compensate for the instability of visual information, and establish multimodal cross-relations through the fusion of visual and language features. Secondly, we propose an attention-based token screening mechanism that utilizes semantic-guided attention and masking operations to eliminate irrelevant search tokens devoid of target information, thereby enhancing both accuracy and efficiency. Furthermore, we optimize the localization head by introducing channel attention, which effectively improves the accuracy of target positioning. Extensive experiments conducted on the OTB99, LaSOT, and TNL2K datasets demonstrate the effectiveness of our proposed tracking method, achieving success rates of 71.2%, 69.5%, and 58.9%, respectively.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104644"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571651","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}

引用次数: 0

Enhancing aesthetic image generation with reinforcement learning guided prompt optimization in stable diffusion 在稳定扩散中，用强化学习引导提示优化增强美学图像的生成

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-15 DOI: 10.1016/j.jvcir.2025.104641

Junyong You , Yuan Lin , Bin Hu

{"title":"Enhancing aesthetic image generation with reinforcement learning guided prompt optimization in stable diffusion","authors":"Junyong You , Yuan Lin , Bin Hu","doi":"10.1016/j.jvcir.2025.104641","DOIUrl":"10.1016/j.jvcir.2025.104641","url":null,"abstract":"<div><div>Generative models, e.g., stable diffusion, excel at producing compelling images but remain highly dependent on crafted prompts. Refining prompts for specific objectives, especially aesthetic quality, is time-consuming and inconsistent. We propose a novel approach that leverages LLMs to enhance prompt refinement process for stable diffusion. First, we propose a model to predict aesthetic image quality, examining various aesthetic elements in spatial, channel, and color domains. Reinforcement learning is employed to refine the prompt, starting from a rudimentary version and iteratively improving them with LLM’s assistance. This iterative process is guided by a policy network updating prompts based on interactions with the generated images, with a reward function measuring aesthetic improvement and adherence to the prompt. Our experimental results demonstrate that this method significantly boosts the visual quality of generated images when using these refined prompts. Beyond image synthesis, this approach provides a broader framework for improving prompts across diverse applications with the support of LLMs.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104641"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571650","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}

引用次数: 0

Cross-distance near-infrared face recognition 跨距离近红外人脸识别

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-17 DOI: 10.1016/j.jvcir.2025.104691

Da Ai , Yunqiao Wang , Kai Jia , Zhike Ji , Ying Liu

{"title":"Cross-distance near-infrared face recognition","authors":"Da Ai , Yunqiao Wang , Kai Jia , Zhike Ji , Ying Liu","doi":"10.1016/j.jvcir.2025.104691","DOIUrl":"10.1016/j.jvcir.2025.104691","url":null,"abstract":"<div><div>In the actual video surveillance application scenarios, the imaging difference between near infrared (NIR) and visible light (VIS) spectrum and the photo distance are two important factors that restrict the accuracy of near infrared face recognition. In this paper, we first use a fixed focus near-infrared camera to capture NIR face images at different distances, constructing a large Cross-Spectral and Cross-Distance Face dataset (CSCD-F), and in order to improve recognition accuracy, we employ image enhancement techniques to preprocess low-quality face images. Furthermore, we adjusted the sampling depth of the generator in the CycleGAN network and introduced additional edge loss, proposing a general framework that combines generative models and transfer learning to achieve spectral feature translation between NIR and VIS images. The proposed method can effectively convert NIR face images into VIS images while retaining sufficient identity information. Various experimental results demonstrate that the proposed method achieves significant performance improvements on the self-built CSCD-F dataset. Additionally, it validates the generalization capability and effectiveness of the proposed method on public datasets such as HFB and Oulu-CASIA NIR-VIS.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104691"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840341","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}

引用次数: 0

HySaM: An improved hybrid SAM and Mask R-CNN for underwater instance segmentation HySaM：一种改进的混合SAM和Mask R-CNN用于水下实例分割

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-25 DOI: 10.1016/j.jvcir.2025.104656

Xingfa Wang , Chengjun Chen , Chenggang Dai , Kunhua Liu , Mingxing Lin

{"title":"HySaM: An improved hybrid SAM and Mask R-CNN for underwater instance segmentation","authors":"Xingfa Wang , Chengjun Chen , Chenggang Dai , Kunhua Liu , Mingxing Lin","doi":"10.1016/j.jvcir.2025.104656","DOIUrl":"10.1016/j.jvcir.2025.104656","url":null,"abstract":"<div><div>Due to inherent absorption and scattering effects, underwater images often exhibit low visibility and significant color deviation. These issues hinder the extraction of discriminative features and adversely impact instance-level segmentation accuracy. To address these challenges, this study proposes a novel Hybrid SAM and Mask R-CNN framework for underwater instance segmentation, integrating the strong generalization capability of SAM with the structural decoding strength of Mask R-CNN. The powerful global modeling ability of SAM effectively mitigates the impact of underwater image degradation, thereby enabling more robust feature representation. Moreover, a novel underwater feature weighted enhancer is introduced in the framework to enhance multi-scale feature fusion and improve the detection of small and scale-varying objects in underwater environments. To provide benchmark data, a large-scale underwater instance segmentation dataset, UW10K, is also constructed, comprising 13,551 images and 22,968 annotated instances across 15 categories. Comprehensive experiments validate the superiority of the proposed model across various instance segmentation tasks. Specifically, it achieves precisions of 74.2 %, 40.5 %, and 70.6 % on UW10K, USIS10K, and WHU Building datasets, respectively. This study is expected to advance ocean exploration and fisheries, while providing valuable training samples for instance segmentation tasks. Datasets and codes are available at <span><span>https://github.com/xfwang-qut/HySaM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104656"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694273","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}

引用次数: 0