Journal of Visual Communication and Image Representation最新文献_第8页

Facial image super-resolution network for confusing arbitrary gender classifiers 用于混淆任意性别分类器的面部图像超分辨率网络

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-11 DOI: 10.1016/j.jvcir.2025.104642

Jiliang Wang , Jia Liu , Siwang Zhou

{"title":"Facial image super-resolution network for confusing arbitrary gender classifiers","authors":"Jiliang Wang , Jia Liu , Siwang Zhou","doi":"10.1016/j.jvcir.2025.104642","DOIUrl":"10.1016/j.jvcir.2025.104642","url":null,"abstract":"<div><div>Existing facial image super-resolution methods have identified the capacity to transform low-resolution facial images into high-resolution ones. However, clearer high-resolution facial images increase the possibility of accurately extracting soft biometric features, such as gender, posing a significant risk of privacy leakage. To address this issue, we propose a gender-protected face super-resolution network, which can incorporate gender-identified privacy information by introducing fine image distortion during the super-resolution process. It progressively transforms low-resolution images into high-resolution ones while partially disturbing the face images. This procedure ensures that the generated super-resolution facial images can still be utilized by face matchers for matching purposes, but are less reliable for attribute classifiers that attempt to extract gender features. Furthermore, we introduce leaping adversarial learning to help the super-resolution network to generate gender-protected facial images and work on arbitrary gender classifiers. Extensive experiments have been conducted using multiple face matchers and gender classifiers to evaluate the effectiveness of the proposed network. The results also demonstrate that our proposed image super-resolution network is adaptable to arbitrary attribute classifiers for protecting gender privacy, while preserving facial image quality.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104642"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520918","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

Multi-modal deep facial expression recognition framework combining knowledge distillation and retrieval-augmented generation 结合知识蒸馏和检索增强生成的多模态深度面部表情识别框架

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.2025.104645

Beibei Jiang, Yu Zhou

{"title":"Multi-modal deep facial expression recognition framework combining knowledge distillation and retrieval-augmented generation","authors":"Beibei Jiang, Yu Zhou","doi":"10.1016/j.jvcir.2025.104645","DOIUrl":"10.1016/j.jvcir.2025.104645","url":null,"abstract":"<div><div>In recent years, significant progress has been made in facial expression recognition (FER) methods based on deep learning. However, existing models still face challenges in terms of computational efficiency and generalization performance when dealing with diverse emotional expressions and complex environmental variations. Recently, large-scale vision-language pre-training models such as CLIP have achieved remarkable success in multi-modal learning. Their rich visual and textual representations offer valuable insights for downstream tasks. Consequently, transferring the knowledge to develop efficient and accurate facial expression recognition (FER) systems has emerged as a key research direction. To the end, this paper proposes a novel model, termed Knowledge Distillation and Retrieval-Augmented Generation (KDRAG), which combines Distillation and Retrieval-Augmented Generation (RAG) techniques to improve the efficiency and accuracy of FER. Through knowledge distillation, the teacher model (ViT-L/14) transfers its rich knowledge to the smaller student model (ViT-B/32). An additional linear projection layer is added to map the teacher model’s output features to the student model’s feature dimensions for feature alignment. Moreover, the RAG mechanism is developed to enhance the emotional understanding of students by retrieving text descriptions related to the input image. Additionally, this framework combines soft loss (from the teacher model’s knowledge) and hard loss (from the true targets of the labels) to enhance the model’s generalization ability. Extensive experimental results on multiple datasets demonstrate that the KDRAG framework can achieve significant improvements in accuracy and computational efficiency, providing new insights for real-time FER systems.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104645"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520897","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

Visual saliency fixation via deeply tri-layered multi blended trans-encoder framework 基于深度三层多混合编码器框架的视觉显著性固定

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-13 DOI: 10.1016/j.jvcir.2025.104676

S. Caroline , Y.Jacob Vetha Raj

{"title":"Visual saliency fixation via deeply tri-layered multi blended trans-encoder framework","authors":"S. Caroline , Y.Jacob Vetha Raj","doi":"10.1016/j.jvcir.2025.104676","DOIUrl":"10.1016/j.jvcir.2025.104676","url":null,"abstract":"<div><div>The capacity to guess where viewers look while reviewing a scene, likewise called saliency prediction or observation, has created critical interest in the fields of computer vision. Incorporating saliency prediction modeling into traditional CNN-based models is challenging. To address this, we developed the Deeply Tri-Layered Multi-Blended Trans-Encoder Framework (DTMBTE) to improve human eye fixation prediction in image saliency tasks. Unlike existing CNN-based methods that struggle with contextual encoding, our model integrates local feature extraction with global attention mechanisms to more accurately forecast saliency regions. We created a new <em>trans</em>-encoder called the Multi Blended Trans-Encoder (MBTE) by combining three different convolution types with encoders that use multiple heads of attention, which effectively localize the human eye fixation or saliency area. This combined design efficiently extracts both spatial and contextual information for saliency estimation. Experiments on MIT1003 and CAT2000 show that DTMBTE outperforms NSS and SIM scores and minimum EMD.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104676"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840343","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

FFTDiff: Tuning-free image texture transfer based on diffusion model FFTDiff：基于扩散模型的免调优图像纹理传输

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-22 DOI: 10.1016/j.jvcir.2025.104681

Shilin Li, Hao Wang, Anna Zhu

{"title":"FFTDiff: Tuning-free image texture transfer based on diffusion model","authors":"Shilin Li, Hao Wang, Anna Zhu","doi":"10.1016/j.jvcir.2025.104681","DOIUrl":"10.1016/j.jvcir.2025.104681","url":null,"abstract":"<div><div>Image texture transfer is pivotal in computer vision, holding extensive application potential. Existing methods typically transfer color alongside texture, lacking inherent color preservation and thus requiring a cumbersome two-stage process: color alignment followed by style transfer. The recent emergence of diffusion models has significantly advanced this field; however, current diffusion-based approaches usually necessitate additional training. To address this, we propose FFTDiff, a novel texture transfer model leveraging pre-trained diffusion models and the Fast Fourier Transform (FFT), eliminating extra training requirements. FFTDiff disentangles texture from content and color within the frequency domain, independently extracting texture from reference images while preserving original colors and semantics. This extracted texture is then seamlessly integrated into the content image within the diffusion model’s latent space during denoising. Comprehensive experimental results demonstrate FFTDiff’s effectiveness, highlighting its capability for realistic, aesthetically pleasing texture transfer without compromising the original semantic content or color integrity.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104681"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840342","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

SAM-FireAdapter: An adapter for fire segmentation with SAM SAM- fireadapter：使用SAM进行火灾分割的适配器

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.2025.104678

Yanan Wu, Chaoqun Hong, Yongfeng Chen, Haixi Cheng

{"title":"SAM-FireAdapter: An adapter for fire segmentation with SAM","authors":"Yanan Wu, Chaoqun Hong, Yongfeng Chen, Haixi Cheng","doi":"10.1016/j.jvcir.2025.104678","DOIUrl":"10.1016/j.jvcir.2025.104678","url":null,"abstract":"<div><div>With the rise of large foundation models, significant advancements have been made in the field of artificial intelligence. The Segment Anything Model (SAM) was specifically designed for image segmentation. However, experiments have demonstrated that SAM may encounter performance limitations in handling specific tasks, such as fire segmentation. To address this challenge, our study explores solutions to effectively adapt the pre-trained SAM model for fire segmentation. The adapter-enhanced approach is introduced to SAM, incorporating effective adapter modules into the segmentation network. The resulting approach, SAM-FireAdapter, incorporates fire-specific features into SAM, significantly enhancing its performance on fire segmentation. Additionally, we propose Fire-Adaptive Attention (FAA), a lightweight attention mechanism module to enhance feature representation. This module reweights the input features before decoding, emphasizing critical spatial features and suppressing less relevant ones. Experimental results demonstrate that SAM-FireAdapter surpasses existing fire segmentation networks including the base SAM.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104678"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797411","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

3D human mesh recovery: Comparative review, models, and prospects 三维人体网格恢复：比较回顾，模型和前景

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2026-01-03 DOI: 10.1016/j.jvcir.2025.104699

Wonjun Kim

{"title":"3D human mesh recovery: Comparative review, models, and prospects","authors":"Wonjun Kim","doi":"10.1016/j.jvcir.2025.104699","DOIUrl":"10.1016/j.jvcir.2025.104699","url":null,"abstract":"<div><div>As a demand for immersive services increases in various fields, the ability to express objects or scenes in 3D has become essential. In particular, 3D human modeling has gained considerable attentions due to its plentiful possibilities for daily life as well as industrial applications. The first step of 3D human modeling is to restore a mesh, which is commonly defined as a set of connected vertices in the 3D space, from images and videos. This is so-called human mesh recovery (HMR). Such HMR has been studied based on complicated optimization techniques, however, owing to the great success of deep learning in recent years, it has been reformulated as a simple regression problem, thus numerous studies are now being actively conducted. This paper aims at providing a comprehensive review with a special focus on deep learning-based methods for HMR. Specifically, this paper covers a systematic taxonomy along with questions at the heart of each research period, diverse methodologies, and abundant performance evaluations on benchmark datasets both qualitatively and quantitatively, and also gives constructive discussions for realization of HMR-based commercialization services. This review is expected to serve as a concise handbook to HMR rather than a vast collection of existing studies.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104699"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884298","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

Turbo principles meet compression: Rethinking nonlinear transformations in learned image compression Turbo原理满足压缩：重新思考学习图像压缩中的非线性变换

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.2025.104643

Chao Li , Wen Tan , Fanyang Meng , Runwei Ding , Ye Wang , Wei Liu , Yongsheng Liang

{"title":"Turbo principles meet compression: Rethinking nonlinear transformations in learned image compression","authors":"Chao Li , Wen Tan , Fanyang Meng , Runwei Ding , Ye Wang , Wei Liu , Yongsheng Liang","doi":"10.1016/j.jvcir.2025.104643","DOIUrl":"10.1016/j.jvcir.2025.104643","url":null,"abstract":"<div><div>Learned image compression (LIC) has emerged as a powerful approach for achieving high rate–distortion performance. Most existing LIC techniques attempt to address performance limitations associated with downsampling and quantization-induced information loss by employing intricate nonlinear transformations and increasing the feature dimensions in entropy models. In this paper, we introduce a novel perspective by modeling the quantizer as a generalized channel with uniform noise, shifting LIC design toward minimizing the channel’s negative impact on compact feature representations. Drawing inspiration from turbo codes, we propose a turbo-like nonlinear transformation (TLNT). On the encoder side, TLNT-E disperses information loss through parallel component coding units, random interleaving, and puncturing, preserving the integrity of encoded features. At the decoder side, TLNT-D iteratively refines feature representations through interactive processing, enabling accurate reconstruction. Experimental results show that our method outperforms several state-of-the-art nonlinear transformation techniques while maintaining efficiency in parameter count and computational complexity.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104643"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571649","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

Blind compressed image diffusion restoration based on content prior and dense residual connection driven transformer 基于内容先验和密集残差连接驱动变压器的压缩图像扩散盲恢复

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-10 DOI: 10.1016/j.jvcir.2025.104674

Shuang Yue, Zhe Chen, Fuliang Yin

{"title":"Blind compressed image diffusion restoration based on content prior and dense residual connection driven transformer","authors":"Shuang Yue, Zhe Chen, Fuliang Yin","doi":"10.1016/j.jvcir.2025.104674","DOIUrl":"10.1016/j.jvcir.2025.104674","url":null,"abstract":"<div><div>JPEG blind compressed image restoration (CIR) aims to restore high-quality images from compressed low-quality images, which is a long-standing low-level visual problem in the image processing. The existing blind CIR methods often overlook basic content details, leading to degradation of restoration quality of blind compressed images. To address this issue, this paper proposes a blind compressed image diffusion restoration model (BCDR) based on content prior and dense residual connection driven transformer. Specifically, we first utilize the image content restoration prior (ICP) learned in low-quality and high-quality images to refine the detail features. Then, the diffusion model estimator is used to reconstruct the texture of images and enhance the visual coherence. Finally, the dense residual connection is applied to capture global information and generate more realistic image details. The proposed model can greatly improve the image quality of blind compressed images and perform well in restoring image content details. The experimental results demonstrate that the proposed method exhibits excellent performance in both the benchmark dataset and the blind CIR task in real-world scenarios.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104674"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748548","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

Theft model-based black-box adversarial attack in embedding space 基于盗窃模型的嵌入空间黑盒对抗攻击

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jvcir.2025.104702

Rui Zhang , Shuliang Jiang , Zi Kang , Shuo Xu , Yuanlong Lv , Hui Xia

引用次数: 0

Detecting human-object interactions with image category-guided and query denoising 使用图像分类引导和查询去噪来检测人与物体的交互

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-01 DOI: 10.1016/j.jvcir.2025.104660

Jing Han , Hongyu Li , Xiaoying Wang , Xueqiang Lyu , Zangtai Cai , Yuzhong Chen

{"title":"Detecting human-object interactions with image category-guided and query denoising","authors":"Jing Han , Hongyu Li , Xiaoying Wang , Xueqiang Lyu , Zangtai Cai , Yuzhong Chen","doi":"10.1016/j.jvcir.2025.104660","DOIUrl":"10.1016/j.jvcir.2025.104660","url":null,"abstract":"<div><div>Existing Detection Transformer (DETR)-based algorithms in Human-Object Interaction (HOI) schemes learn instance-level human-object pair features to infer interaction behaviors, ignoring the influence of image macrostructure on the interaction behaviors. Meanwhile, the instability of the Hungarian matching process affects model convergence. Spurred by these concerns, this paper presents a novel HOI detection method featuring image category guidance and enhanced by query denoising. The proposed method constructs an image-level category query, which enhances the instance-level query based on the image-level contextual features to infer the interactions between humans and objects. Additionally, we introduce a query denoising training mechanism. Controlled noise is added to ground-truth queries, and the model is trained to reconstruct the original targets. This approach stabilizes matching and accelerates convergence. Furthermore, a branching shortcut is added to the triplet Hungarian matching process to stabilize the model’s training process. Experiments on the HICO-DET and V-COCO datasets demonstrate the superior performance of our method. Our method achieves accuracies of 37.71% on HICO-DET and 67.1% on V-COCO, while reducing training rounds from 500 to 25. The 95% reduction in training time results in significantly lower computational costs and energy consumption, enhancing the feasibility of practical deployment and accelerating experimental cycles. The code is available at <span><span>https://github.com/lihy000/CADN-HOTR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104660"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748546","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