Image and Vision Computing最新文献

{"title":"OIDSty: One-shot identity-preserving face stylization","authors":"Kairui Wang ,&nbsp;Xinying Liu ,&nbsp;Di Zhao ,&nbsp;Xuelei Geng ,&nbsp;Tian Xian ,&nbsp;Yonghao Chang","doi":"10.1016/j.imavis.2026.105899","DOIUrl":"10.1016/j.imavis.2026.105899","url":null,"abstract":"<div><div>In recent years, image generation techniques based on diffusion models have made significant progress in the field of facial stylization. However, existing methods still face challenges in achieving high identity fidelity while maintaining strong stylistic expressiveness, particularly in balancing the geometric deformations introduced by stylization with the preservation of fine facial features (such as facial features and poses). To address this issue, this paper proposes a novel single-sample facial stylization system—OIDSty. Its core innovation lies in decoupling identity preservation and style injection tasks across distinct attention layers, primarily achieved through two key designs: (1) High-Fidelity Identity Module, which innovatively combines strong semantic conditions and weak spatial conditions to guide cross-attention layers. This design enables precise retention of core identity and facial layout features while permitting stylized geometric deformations; (2) The DINO-Style Texture Guidance Module introduces this loss function into the self-attention layer to compute the feature difference between the ideal stylized output and the current output. This loss is integrated into the denoising sampling process, dynamically calibrating latent features through gradients to ensure efficient and accurate transfer of stylized textures onto the target image. Extensive experimental results demonstrate that OIDSty generates high-fidelity, stylistically distinct images across multiple styles. Compared to existing state-of-the-art methods, our method exhibits significant advantages across all objective and subjective evaluation metrics without requiring complex parameter tuning.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105899"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"MMDehazeNet: Cross-modality attention with feature correction and multi-scale encoding for visible-infrared dehazing","authors":"Liangliang Duan","doi":"10.1016/j.imavis.2026.105896","DOIUrl":"10.1016/j.imavis.2026.105896","url":null,"abstract":"<div><div>Haze-induced image degradation significantly degrades visual quality and impairs the performance of outdoor computer vision systems. Traditional single-image dehazing methods suffer from inherent limitations in dense haze scenarios due to the ill-posed nature of the problem. Leveraging complementary information from visible (RGB) and near-infrared (NIR) modalities offers a robust solution, as NIR signals exhibit superior penetration through atmospheric particles. This paper presents MMDehazeNet, a novel end-to-end multimodal fusion network for visible-infrared image dehazing. Adopting a U-Net-based dual-encoder architecture, it jointly processes hazy RGB and NIR images, with three key innovations: (1) a Gated Cross-Modality Attention (GCMA) module for efficient multi-level fusion; (2) a Multimodal Feature Correction (MMFC) module with a learned gating mechanism for adaptive inter-modal alignment; and (3) Multi-Scale Convolutional Layers (MSCL) for multi-receptive field feature extraction. Three variants (i.e., MMDehazeNet-S, -B, -L) are proposed. Extensive evaluations on the AirSim-VID, EPFL, and FANVID datasets demonstrate that MMDehazeNet achieves state-of-the-art performance. Quantitative and qualitative comparisons validate its significant superiority over existing single- and multi-modal methods, particularly under challenging medium and dense haze conditions.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105896"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"MSTVQA: A multi-path dynamic perception method for video quality assessment","authors":"Junwei Qi ,&nbsp;Yingzhen Wang ,&nbsp;Jingpeng Gao ,&nbsp;Yichen Wu ,&nbsp;Pujiang Liu","doi":"10.1016/j.imavis.2025.105891","DOIUrl":"10.1016/j.imavis.2025.105891","url":null,"abstract":"<div><div>The proliferation of self-media and smart devices has led to uneven video quality on streaming platforms, so there is an urgent need for effective automated video quality assessment (VQA) methods. But most existing VQA methods fail to fully consider dynamic adaptability of the human visual perception system and its synergistic mechanism. In this study, we proposed a novel multi-path sensing framework for VQA to enhance the progressive sensing capability of the model. Specifically, the complete video has to be divided into three perceptual levels: patch clips, sampled frame stream, and inter-frame differences, a balance factor is used to give different levels of perceptual weights. Firstly, the purpose of defining a patch sampling method is to reduce the input data of the model while aligning temporal information, to extract subtle motion features in patch clips. After that, to further enhance the representation of local high-frequency details, the global variance-guided temporal dimension attention mechanism and spatial feature aggregation pool are used to accurately fit the sampling frame sequence. Finally, by embedding the feature map differences between consecutive frames and utilizing the long-term spatio-temporal dependence of Transformer to simulate the global dynamic evolution, the model achieves progressive interaction of cross scale spatio-temporal information. In addition, the improved temporal hysteresis pool enhances the ability to capture nonlinear dynamics in time series data and can more faithfully simulate subtle changes in the human visual perception system. Experimental results show that the proposed method outperforms existing no-reference VQA (NR-VQA) approaches across five in-the-wild datasets. In particular, it achieves outstanding performance on the CVD2014 dataset, which is the smallest in scale and contains the fewest scene variations, reaching a PLCC of 0.927 and an SRCC of 0.925. These results clearly demonstrate the effectiveness and advantages of our method in the VQA task.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105891"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Enhancing UAV small target detection: A balanced accuracy-efficiency algorithm with tiered feature focus","authors":"Hanwei Guo,&nbsp;Shugang Liu","doi":"10.1016/j.imavis.2026.105897","DOIUrl":"10.1016/j.imavis.2026.105897","url":null,"abstract":"<div><div>Small target detection in unmanned aerial vehicle (UAV) imagery is crucial for both military and civilian applications. However, achieving a balance between detection performance, efficiency, and lightweight architecture remains challenging. This paper introduces TF-DEIM-DFINE, a tiered focused small target detection model designed specifically for UAV tasks.We propose the Convolutional Gated-Visual Mamba (CG-VIM) module to enhance global dependency capture and local detail extraction through long sequence modeling, along with the Half-Channel Single-Head Attention (HCSA) module for global modeling, which improves fine-grained representation while reducing computational redundancy. Additionally, our Tiered Focus-Feature Pyramid Networks (TF-FPN) improve the representational capability of high-frequency information in multi-scale features without significantly increasing computational overhead. Experimental results on the VisDrone dataset demonstrate a 4.7% improvement in AP<span><math><msub><mrow></mrow><mrow><mtext>M</mtext></mrow></msub></math></span> and a 5.8% improvement in AP metrics, with a 37% reduction in parameter count and only a 6% increase in GFLOPs, maintaining unchanged FPS. These results highlight TF-DEIM-DFINE’s ability to improve detection accuracy while preserving a lightweight and efficient structure</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105897"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Disentangling co-occurrence with class-specific banks for Weakly Supervised Semantic Segmentation","authors":"Hang Yao,&nbsp;Yuanchen Wu,&nbsp;Kequan Yang,&nbsp;Jide Li,&nbsp;Chao Yin,&nbsp;Zihang Li,&nbsp;Xiaoqiang Li","doi":"10.1016/j.imavis.2025.105893","DOIUrl":"10.1016/j.imavis.2025.105893","url":null,"abstract":"<div><div>In Weakly Supervised Semantic Segmentation (WSSS), co-occurring objects often degrade the quality of Class Activation Maps (CAMs), ultimately compromising segmentation accuracy. Many recent WSSS methods leverage Contrastive Language-Image Pre-training (CLIP) by contrasting target-class images with text descriptions of background classes, thus providing additional supervision. However, these methods only rely on a shared background class set across all target classes, ignoring that each class has its own unique co-occurring objects. To resolve this limitation, this paper proposes a novel method that constructs semantically related class banks for each target class to <strong>di</strong>sentangle <strong>co</strong>-occurring objects (dubbed <strong>DiCo</strong>). Specifically, DiCo first uses Large Language Models (LLMs) to generate semantically related class banks for each target class, which are further divided into negative and positive class banks to form contrastive pairs. The negative class banks include co-occurring objects related to the target class, while the positive class banks consist of the target class itself, along with its super-classes and sub-classes. By contrasting these negative and positive class banks with images through CLIP, DiCo disentangles target classes from co-occurring classes, simultaneously enhancing the semantic representations of the target class. Moreover, different classes have differential contributions to the disentanglement of co-occurring classes. DiCo introduces an adaptive weighting mechanism to adjust the contributions of co-occurring classes. Experimental results demonstrate that DiCo achieves superior performance compared to previous work on PASCAL VOC 2012 and MS COCO 2014.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105893"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"A video anomaly detection and classification method based on cross-modal feature alignment","authors":"Yan Fu,&nbsp;Ting Hou,&nbsp;Ou Ye,&nbsp;Gaolin Ye","doi":"10.1016/j.imavis.2025.105874","DOIUrl":"10.1016/j.imavis.2025.105874","url":null,"abstract":"<div><div>Detecting anomalous behaviors in surveillance videos is crucial for enhancing public safety and industrial monitoring. However, existing methods typically only detect the presence of anomalies without identifying their specific types, making targeted responses difficult. Additionally, these methods fail to effectively capture the dynamic relationship between persistent and sudden anomalies in complex scenarios. To address these issues, we propose an innovative anomaly detection model based on a dual-branch architecture. This model uses a cross-modal alignment mechanism to explicitly associate visual features with semantic concepts, enabling it to discriminate based on interpretable semantic evidence, thereby significantly improving the accuracy of anomaly detection. Specifically, the coarse-grained branch introduces an additive dilated convolution pyramid collaborative module (ADCP) that uniquely replaces traditional large-scale matrix multiplication with additive operations. This module dynamically fuses temporal information at different time scales and avoids the over-mixing of anomaly types, maintaining long-term memory and stable information flow, allowing the model to flexibly capture the relationship between long-term trends and short-term fluctuations. We also design a dynamic smoothing enhancement module (DSE) that uses a weighted average mechanism with sliding windows of different sizes to dynamically integrate features in local periods, filtering out long-term noise and sudden fluctuations, aiding in more precise anomaly boundary detection. The fine-grained branch focuses on semantic information, converting raw text related to anomaly types into category labels and generating learnable prompt text features. By combining these with visual features, cosine similarity is computed to precisely identify anomaly types. Experimental results show significant improvements on the XD-Violence and UCF-Crime datasets.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105874"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"PD-DDPM: Prior-driven diffusion model for single image dehazing","authors":"Haoqin Sun,&nbsp;Jindong Xu,&nbsp;Jiaxin Gong,&nbsp;Yijie Wang","doi":"10.1016/j.imavis.2025.105888","DOIUrl":"10.1016/j.imavis.2025.105888","url":null,"abstract":"<div><div>Haze significantly reduces the visual quality of images, particularly in dense atmospheric conditions, resulting in a substantial loss of perceptible structural and semantic information. This degradation negatively affects the performance of vision-based systems in critical applications such as autonomous navigation and intelligent surveillance. Consequently, single image dehazing has been recognized as a challenging inverse problem, aiming to restore clear images from hazy observations. Although significant progress has been made with existing dehazing approaches, the intrinsic mixing of haze-related features with unrelated image content often leads to distortions in color and detail preservation, limiting restoration accuracy. In recent years, Denoising Diffusion Probabilistic Model (DDPM) has demonstrated excellent performance in image generation and restoration tasks. However, the effectiveness of these methods in single image dehazing remains constrained by both irrelevant image content and temporal redundancy during sampling. To address these limitations, we propose a diffusion model-based dehazing method that effectively recovers image content by integrating both local and global priors through differential convolution. Furthermore, the generative capability of DDPM is exploited to enhance image texture and fine details. To reduce temporal redundancy during the diffusion process, a noise addition strategy based on the Fibonacci Sequence is introduced, which significantly optimizes the sampling time and improves overall computational efficiency. Experimental validation shows that the proposed method requires only 1/5 to 1/6 of the time required by the linear noise addition method. Additionally, the overall network achieves excellent performance in both synthetic and real dehazing datasets.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105888"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"KPTFusion: Knowledge Prior-based Task-Driven Multimodal Image Fusion","authors":"Yubo Fu,&nbsp;Xia Ye,&nbsp;Xinyan Kong","doi":"10.1016/j.imavis.2025.105886","DOIUrl":"10.1016/j.imavis.2025.105886","url":null,"abstract":"<div><div>Multimodal image fusion aims to generate fused images that are richer in information, more credible in content, and perform better in relevant downstream tasks. However, this task typically faces two major challenges: First, due to the lack of fusion ground truth, it is difficult to guide the model’s parameters to converge to the optimal feature distribution without explicit supervision signals; Second, existing methods generally suffer from insufficient intermodal feature interaction, limiting the network’s ability to fully exploit the inherent complementarity of multimodal features. To address these issues, we propose the Knowledge Prior-based Task-Driven Multimodal Image Fusion (KPTFusion) framework. This framework introduces a knowledge prior to approximate the true distribution and sets corresponding task constraints for different downstream tasks, thereby guiding the network’s fusion output to approximate the target distribution. Specifically, we define knowledge prior as the learning objective for the fusion distribution and further design a Task-Perception Constraint Module (TPCM) to guide the network toward the optimal distribution required for specific tasks. Additionally, to enhance intermodal interactions, we embed a Dynamic Cross-Feature Module (DCA) within the network. This module utilizes a dual-stream attention mechanism to strengthen cross-modal feature interactions, ensuring the fused image fully preserves and integrates information from all modalities. Experimental results demonstrate that KPTFusion not only generates visually high-quality fusion outputs in infrared-visible and medical image fusion tasks but also achieves significant performance improvements in downstream tasks such as object detection and semantic segmentation based on the fusion results. This fully validates the effectiveness of its task-oriented fusion approach.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105886"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 global context fusion for camouflaged object detection","authors":"Baichuan Shen ,&nbsp;Yan Dou ,&nbsp;Yaolei Li ,&nbsp;Wenjun Zhang ,&nbsp;Xiaoyan Wang","doi":"10.1016/j.imavis.2026.105915","DOIUrl":"10.1016/j.imavis.2026.105915","url":null,"abstract":"<div><div>Due to the low contrast between camouflaged objects and backgrounds, the diversity of object edge shapes, and occlusions in complex scenes, existing deep learning-based Camouflaged Object Detection (COD) methods still face significant challenges in achieving high-precision detection. These challenges include difficulties in extracting multi-scale detail features for small object detection, modeling global context in occluded scenarios, and accurately distinguishing the boundaries between objects and backgrounds in complex edge detection tasks.To address these issues, this paper proposes MGCF-Net (Multi-level Global Context Fusion Network), a novel approach that integrates multi-scale context learning and feature fusion. The method employs an improved Pyramid Vision Transformer (PVTv2) as the backbone, coupled with a Cross-Scale Self-Attention (CSSA) module and a Multi-scale Fusion Attention (MFA) module. A Guided Alignment Feature Module (GAFM) aligns multi-scale features, while a large-kernel convolution structure (SHRF) enhances the global context capture capability. Experimental results on several COD benchmark data sets show that the proposed method improves 2.2%, 2.1% and 4.9% in structure metric, mean enhancement metric and weighted F metric respectively compared with FEDER, which is the second best overall performance, while the mean absolute error (MAE) decreases by 21.4%. It shows significant advantages in detection accuracy and generalization performance compared with several state-of-the-art methods (SOTA). Additionally, the method demonstrates excellent generalization to related tasks, such as polyp segmentation, COVID-19, lung infection detection, and defect detection.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105915"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Dynamic multi-scenario prompt learning with knowledge augmentation for image emotion analysis","authors":"Tan Chen,&nbsp;Guozeng Zhang,&nbsp;Yiwei Wei,&nbsp;Jialin Chen,&nbsp;Cheng Feng","doi":"10.1016/j.imavis.2025.105884","DOIUrl":"10.1016/j.imavis.2025.105884","url":null,"abstract":"<div><div>Image emotion analysis (IEA) aims to identify and comprehend human emotional states from visual content, which has garnered significant attention due to the growing trend of expressing opinions online. Existing IEA approaches typically attempt to explore the emotional semantic space by generating prompts for images through fixed templates or randomly generated vectors. However, these methods neglect the diverse fine-grained emotions across scenes within the same emotional category, thereby limiting the nuanced expression of emotional semantics. Moreover, fine-grained emotional information is often abstract, and its quantity remains unknown, making its extraction particularly challenging. In light of this issue, we propose a novel approach, Dynamic Multi-Scenario Prompt Learning with Knowledge Augmentation (DMSP-KA). We first design a similarity-based selection mechanism (SSM) to construct fine-grained multi-scenario emotional knowledge for all emotional categories. Subsequently, we integrate the image’s intrinsic semantics with fine-grained emotional knowledge to generate a consistent emotional bias at the composite level, creating dynamic multi-scenario prompts (DMSP) for each instance. Additionally, we leverage predefined emotional texts to assist in building cross-modal semantic associations and enhancing emotional information fusion. Finally, we establish a caching mechanism (CM) based on the multi-scenario knowledge to improve the accuracy of single-emotion classification. Experimental results on four widely used emotion datasets demonstrate that our proposed method outperforms current state-of-the-art (SOTA) approaches, achieving accuracies of 80.68% on FI, 73.74% on EmotioRoI, 92.13% on TwitterI, and 88.72% on TwitterII.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"167 ","pages":"Article 105884"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}