Computer Vision and Image Understanding最新文献_第7页

SASTD: Stepwise attention style transfer network based on diffusion models 基于扩散模型的分步注意风格迁移网络

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-23 DOI: 10.1016/j.cviu.2025.104612

Zhuoya Wang, Gui Chen, Yaxin Li, Yongsheng Dong

{"title":"SASTD: Stepwise attention style transfer network based on diffusion models","authors":"Zhuoya Wang, Gui Chen, Yaxin Li, Yongsheng Dong","doi":"10.1016/j.cviu.2025.104612","DOIUrl":"10.1016/j.cviu.2025.104612","url":null,"abstract":"<div><div>Image style transfer techniques have significantly advanced, aiming to create images that adopt the style attributes of one source while maintaining the spatial layout of another. However, the interrelationship between style and content often causes the problem of information entanglement within the generated stylized result. To alleviate this issue, in this paper we propose a stepwise attention style transfer network based on diffusion models (SASTD). Specifically, we introduce an attention feature extraction and fusion module, which employs a step-by-step injection method to effectively combine the extracted content and style attention features at different time stages. Additionally, we propose a noise initialization module based on adaptive instance normalization (AdaIN) in the early fusion stage to initialize the initial latent noise during image generation, preserving certain initial feature statistics. Furthermore, we incorporate edge attention from the content image to enhance the preservation of its structural details. Finally, we propose a LAB space alignment module to further optimize the initially generated stylized image. This method ensures high-quality style transfer while better maintaining the spatial semantics of the content image. Experimental results demonstrate that our proposed SASTD achieves better performance in both qualitative and quantitative comparisons compared to both image style transfer methods and style-guided text-to-image synthesis methods.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104612"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886184","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}

引用次数: 0

Enhanced approach with edge feature guidance for LiDAR signal denoising 基于边缘特征制导的激光雷达信号去噪方法

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-12 DOI: 10.1016/j.cviu.2025.104609

A. Anigo Merjora , P. Sardar Maran

{"title":"Enhanced approach with edge feature guidance for LiDAR signal denoising","authors":"A. Anigo Merjora , P. Sardar Maran","doi":"10.1016/j.cviu.2025.104609","DOIUrl":"10.1016/j.cviu.2025.104609","url":null,"abstract":"<div><div>This research addresses the challenging task of denoising Light Detection and Ranging (LiDAR) signals, with a specific focus on Rayleigh backscatter signals that are particularly vulnerable to noise due to atmospheric interference and sensor limitations. An enhanced shearlet wavelet U-Net with edge feature guidance is proposed, which is a novel deep learning framework that integrates the multi-directional, multi-scale decomposition capabilities of the Shearlet wavelet transform with the powerful feature extraction and localization properties of the U-Net architecture. A key contribution of this research is the introduction of an edge feature guidance module within the U-Net, designed to preserve critical structural and edge details typically lost during denoising. The denoising process uses the Shearlet transform to decompose the noisy input signal into different scales and orientations. This allows the model to better identify the difference between noise and signal, and, more importantly, make this differentiation based on resolutions. The experimental assessments applied the suggested method to both synthetic and real-world atmospheric LiDAR datasets and compared it with a number of cutting-edge denoising methods, specifically classical wavelet-based denoising methods, as well as supervised deep-learning methods. Quantitative results indicate that our model produces a 28% higher average signal-to-noise ratio (SNR) and a 31% higher mean squared error (MSE) improvement on average than baseline methods. Qualitative analysis shows the proposed model continues to retain small-scale atmospheric structures and edge continuity. Overall, the results indicate that the proposed method is effective for improving LiDAR signal quality implemented for a wide range of applications in environmental monitoring and meteorology, and signal fidelity is critical.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104609"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038534","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}

引用次数: 0

Memory-enriched thought-by-thought framework for complex Diagram Question Answering 记忆丰富的思维框架，复杂的图表问答

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-26 DOI: 10.1016/j.cviu.2025.104608

Xinyu Zhang , Lingling Zhang , Yanrui Wu , Shaowei Wang , Wenjun Wu , Muye Huang , Qianying Wang , Jun Liu

{"title":"Memory-enriched thought-by-thought framework for complex Diagram Question Answering","authors":"Xinyu Zhang , Lingling Zhang , Yanrui Wu , Shaowei Wang , Wenjun Wu , Muye Huang , Qianying Wang , Jun Liu","doi":"10.1016/j.cviu.2025.104608","DOIUrl":"10.1016/j.cviu.2025.104608","url":null,"abstract":"<div><div>Large language models (LLMs) can effectively generate reasoning processes for simple tasks, but they struggle in complex and novel reasoning scenarios. This problem stems from LLMs often fusing visual and textual information in a single step, lacking the capture and representation of key information during the reasoning process, ignoring critical changes in the reasoning process, and failing to reflect the complex and dynamic nature of human-like reasoning. To address these issues, we propose a new framework called <strong>M</strong>emory-<strong>E</strong>nriched <strong>T</strong>hought-by-<strong>T</strong>hought (METbT), which incorporates memory and operators. On the one hand, the memory is used to store intermediate representations of the reasoning process, preserving information from the reasoning steps and preventing the language model from generating illogical text. On the other hand, the introduction of operators offers various methods for merging visual and textual representations, significantly enhancing the model’s ability to learn representations. We develop the METbT-Bert, METbT-T5, METbT-Qwen and METbT-InternLM, leveraging Bert, T5, Qwen and InternLM as the foundational language models with our framework, respectively. Experiments are conducted on multiple datasets including Smart-101, ScienceQA, and IconQA, and in all cases, the results surpassed those of the same language models. The results demonstrate that our METbT framework offers superior scalability and robustness.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104608"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886187","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}

引用次数: 0

CNNs vs Transformers: Confirmatory factor analysis for eye gaze classification with explainable AI cnn vs变形金刚：用可解释的人工智能进行眼睛凝视分类的验证性因素分析

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-31 DOI: 10.1016/j.cviu.2025.104624

Naman Goyal, Major Singh Goraya, Tajinder Singh

{"title":"CNNs vs Transformers: Confirmatory factor analysis for eye gaze classification with explainable AI","authors":"Naman Goyal, Major Singh Goraya, Tajinder Singh","doi":"10.1016/j.cviu.2025.104624","DOIUrl":"10.1016/j.cviu.2025.104624","url":null,"abstract":"<div><div>Recent advancements in eye gaze classification have significant implications for enhancing human robot interaction. Existing benchmark datasets such as UnityEyes often exhibit class imbalance issues, negatively impacting classification efficacy. Addressing this challenge, a balanced dataset, termed reformed-UE, containing 500 images per class across eight distinct gaze directions is introduced. A novel hyperparameter optimized deep learning model, designated <span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>image</mi></mrow></msub></math></span>, is proposed for image-based gaze direction classification. Additionally, the balanced, large-scale MRL dataset enables rigorous generalization testing of the <span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>image</mi></mrow></msub></math></span> model. Comparative evaluations involving state of the art models including MobileNetV2, InceptionNetV3, AttentionCNN, MobileViT, Hybrid PCCR and Swin Transformers are conducted. The <span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>image</mi></mrow></msub></math></span> model achieves superior performance metrics, registering a validation accuracy of 93.75%, exceeding competing models by approximately 4 to 5 percentage points. Furthermore, <span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>image</mi></mrow></msub></math></span> attains higher precision (0.93), recall (0.93), and F1score (0.93), significantly reducing classification errors, particularly in the challenging TopRight class. Interpretability analyses employing Gradient weighted Class Activation Mapping (GradCAM) heatmaps provide further confirmation of the model’s proficiency in identifying essential latent features critical for accurate classification.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104624"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886186","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}

引用次数: 0

AuxFlow: Anchor-grounded homography estimation through flow-guided auxiliary points for Soccer field registration and player localization AuxFlow：通过流引导辅助点对足球场注册和球员定位进行锚接地的单应性估计

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-21 DOI: 10.1016/j.cviu.2026.104662

Julian Ziegler, Daniel Matthes, Patrick Frenzel, Mirco Fuchs

引用次数: 0

BiPG-FER: Bi-intelligence probabilistic graph for facial expression inference drived by action units BiPG-FER：动作单元驱动的面部表情推理的双智能概率图

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-14 DOI: 10.1016/j.cviu.2026.104655

Fei Wan, Ruicong Zhi

{"title":"BiPG-FER: Bi-intelligence probabilistic graph for facial expression inference drived by action units","authors":"Fei Wan, Ruicong Zhi","doi":"10.1016/j.cviu.2026.104655","DOIUrl":"10.1016/j.cviu.2026.104655","url":null,"abstract":"<div><div>Investigating the associations between facial action units (AUs) and emotions (EMOs) helps to eliminate the constraints imposed by predefined emotion patterns. However, accurately modeling the soft, probabilistic AU–EMO relationships and inferring emotional states from AU sequences remains a challenging task. To address this, we propose a Bi-intelligence Probabilistic Graph model (BiPG-FER), which flexibly learns interpretable AU–AU and AU–EMO associations and enables automatic facial expression inference from AU sequences. In the input phase, a small portion of external prior knowledge is incorporated to mitigate the high-entropy fluctuations often caused by random initialization. We construct a two-layer fully connected AU–EMO association graph and develop an end-to-end architecture with a masking mechanism that dynamically updates the AU–AU and AU–EMO relationships by computing joint probabilities. An oversampling strategy, combined with a adaptive thresholding and a data-contribution-aware reweighting scheme, is introduced to address the skewed post-distribution of emotion labels. Finally, we design a strategy that preserves previous model weights and generates pseudo-samples based on the top-k conditional AU–EMO probabilities, allowing the model to evolve smoothly in a continuously changing and heterogeneous data stream. Experimental results demonstrate that the proposed BiPG-FER effectively produces interpretable probabilistic associations while improving recognition performance on both micro-expression and macro-expression datasets.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104655"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978916","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}

引用次数: 0

TI-PREGO: Chain of Thought and In-Context Learning for online mistake detection in PRocedural EGOcentric videos TI-PREGO：程序性自我中心视频中在线错误检测的思维链和语境学习

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1016/j.cviu.2025.104613

Leonardo Plini , Luca Scofano , Edoardo De Matteis , Guido Maria D’Amely di Melendugno , Alessandro Flaborea , Andrea Sanchietti , Giovanni Maria Farinella , Fabio Galasso , Antonino Furnari

{"title":"TI-PREGO: Chain of Thought and In-Context Learning for online mistake detection in PRocedural EGOcentric videos","authors":"Leonardo Plini , Luca Scofano , Edoardo De Matteis , Guido Maria D’Amely di Melendugno , Alessandro Flaborea , Andrea Sanchietti , Giovanni Maria Farinella , Fabio Galasso , Antonino Furnari","doi":"10.1016/j.cviu.2025.104613","DOIUrl":"10.1016/j.cviu.2025.104613","url":null,"abstract":"<div><div>Identifying procedural errors online from egocentric videos is a critical yet challenging task across various domains, including manufacturing, healthcare and skill-based training. The nature of such mistakes is inherently open-set, as unforeseen or novel errors may occur, necessitating robust detection systems that do not rely on prior examples of failure. Currently, no existing technique can reliably detect open-set procedural mistakes in an online setting. We propose a dual-branch architecture to address this problem in an online fashion: the recognition branch takes input frames from egocentric video, predicts the current action and aggregates frame-level results into action tokens while the anticipation branch leverages the solid pattern-matching capabilities of Large Language Models (LLMs) to predict action tokens based on previously predicted ones. Mistakes are detected as mismatches between the currently recognized action and the action predicted by the anticipation module.</div><div>Extensive experiments on two novel procedural datasets demonstrate the challenges and opportunities of leveraging a dual-branch architecture for mistake detection, showcasing the effectiveness of our proposed approach.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104613"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928067","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}

引用次数: 0

Local–global collaborative feature learning with level-wise decoding for infrared small target detection 基于分层解码的局部-全局协同特征学习红外小目标检测

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.cviu.2026.104636

Weiwei Duan, Luping Ji, Shengjia Chen, Jianghong Huang

{"title":"Local–global collaborative feature learning with level-wise decoding for infrared small target detection","authors":"Weiwei Duan, Luping Ji, Shengjia Chen, Jianghong Huang","doi":"10.1016/j.cviu.2026.104636","DOIUrl":"10.1016/j.cviu.2026.104636","url":null,"abstract":"<div><div>Due to the low signal-to-noise ratio and weak visual contrast, infrared small targets are often submerged in the background. Therefore, it is crucial to preserve target information while extracting distinctive features that distinguish them from the background. However, existing methods generally rely on convolutions and transformers in isolation, which limits their ability to capture robust target features in complex scenes. To address this issue, we propose a new local–global feature collaborative learning (LGFC) framework. It could adequately integrate the local spatial features with the global context of targets in a unified manner. Specifically, we develop an enhanced <em>Gaussian-mask Vision Transformer</em> group with <em>Global Gaussian Attention</em> and <em>Local Window Attention</em> to extract refined global features. The local coarse features obtained from the convolution encoder are then coordinated with the refined global features through <em>Local–Global Collaborating</em>. Moreover, to avoid feature loss during decoding, we propose a level-wise decoding strategy with <em>Cross-layer Feature Interaction</em> to to mitigate information loss in deep networks. Additionally, we introduce a <em>Coarse-to-Fine Refinement</em> post-processing mechanism to improve the precision of target contours. The extensive experiments on three public datasets (NUAA-SIRST, IRSTD-1K and SIRST-AUG) demonstrate the superiority and generalization ability of our proposed LGFC framework for infrared small target detection, outperforming state-of-the-art methods by approximately 2.3% in F1-score on each dataset.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104636"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928062","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}

引用次数: 0

CSN: A compact semantic segmentation network for visual scene perception in assistive navigation 一种用于辅助导航视觉场景感知的紧凑语义分割网络

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-22 DOI: 10.1016/j.cviu.2026.104665

Yunjia Lei , Son Lam Phung , Yang Di , Abdesselam Bouzerdoum

{"title":"CSN: A compact semantic segmentation network for visual scene perception in assistive navigation","authors":"Yunjia Lei , Son Lam Phung , Yang Di , Abdesselam Bouzerdoum","doi":"10.1016/j.cviu.2026.104665","DOIUrl":"10.1016/j.cviu.2026.104665","url":null,"abstract":"<div><div>Accuracy and efficiency are essential in assistive navigation algorithms to ensure accessibility and reliability for visually impaired individuals. However, existing deep learning models often require substantial computational resources to achieve high accuracy, making them impractical for deployment on mobile devices. To address this problem, we introduce CSN, a compact semantic segmentation network designed for assistive navigation, optimizing performance in resource-constrained environments. With CSN, we introduce two innovative modules, the cascaded atrous multi-scale enhancement (CAME) layer and the dual-path residual bottleneck (DPRB) block. The CAME layer efficiently enhances multi-scale representation through feature resampling, while the DPRB block improves feature refinement with minimal computational cost. These modules enable CSN to achieve robust and reliable segmentation across diverse and complex pedestrian environments. The proposed approach achieves the best result on the challenging TrueSight dataset, demonstrating superior prediction accuracy and computational efficiency compared to state-of-the-art lightweight models. CSN achieves a mean intersection over union of 60.99%, while maintaining a low computational cost of 83.46 giga floating-point operations, a compact model size of 8.41 million parameters, and a real-time inference speed of 52.59 frames per second.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104665"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038532","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}

引用次数: 0

Cascaded-parallel decoders and anchor-guided query generator for human-object interaction 用于人-物交互的级联并行解码器和锚引导查询生成器

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-12 DOI: 10.1016/j.cviu.2026.104648

Hang Su , Hong-Bo Zhang , Jia-Yin Luo , Jing-Hua Liu , Zhen-Zhen Sun , Ji-Xiang Du

引用次数: 0