CAAI Transactions on Intelligence Technology最新文献

{"title":"Multi-Scale Transformer for Image Restoration","authors":"Wuzhen Shi,&nbsp;Youwei Pan,&nbsp;Chun Zhao,&nbsp;Yuqing Liu,&nbsp;Shaobo Zhang,&nbsp;Heng Zhang,&nbsp;Yang Wen","doi":"10.1049/cit2.70079","DOIUrl":"https://doi.org/10.1049/cit2.70079","url":null,"abstract":"<p>Although Transformer-based image restoration methods have demonstrated impressive performance, existing Transformers still insufficiently exploit multiscale information. Previous non-Transformer-based studies have shown that incorporating multiscale features is crucial for improving restoration results. In this paper, we propose a multiscale Transformer (MST) that captures cross-scale attention among tokens, thereby effectively leveraging the multiscale patch recurrence prior of natural images. Furthermore, we introduce a channel-gate feed-forward network (CGFN) to enhance inter-channel information aggregation and reduce channel redundancy. To simultaneously utilise global, local and multiscale features, we design a multitype feature integration block (MFIB). Extensive experiments on both image super-resolution and HEVC compressed video artefact reduction demonstrate that the proposed MST achieves state-of-the-art performance. Ablation studies further verify the effectiveness of each proposed module.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"41-54"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AVCLNet: Multimodal Multispeaker Tracking Network Using Audio-Visual Contrastive Learning","authors":"Yihan Li,&nbsp;Yidi Li,&nbsp;Zhenhuan Xu,&nbsp;Hao Guo,&nbsp;Mengyuan Liu,&nbsp;Weiwei Wan","doi":"10.1049/cit2.70092","DOIUrl":"10.1049/cit2.70092","url":null,"abstract":"<p>Audio-visual speaker tracking aims to determine the locations of multiple speakers in the scene by leveraging signals captured from multisensor platforms. Multimodal fusion methods can improve both the accuracy and robustness of speaker tracking. However, in complex multispeaker tracking scenarios, critical challenges such as cross-modal feature discrepancy, weak sound source localisation ambiguity and frequent identity switch errors remain unresolved, which severely hinder the modelling of speaker identity consistency and consequently lead to degraded tracking accuracy and unstable tracking trajectories. To this end, this paper proposes a multimodal multispeaker tracking network using audio-visual contrastive learning (AVCLNet). By integrating heterogeneous modal representations into a unified space through audio-visual contrastive learning, which facilitates cross-modal feature alignment, mitigates cross-modal feature bias and enhances identity-consistent representations. In the audio-visual measurement stage, we design a vision-guided weak sound source weighted enhancement method, which leverages visual cues to establish cross-modal mappings and employs a spatiotemporal dynamic weighted mechanism to improve the detectability of weak sound sources. Furthermore, in the data association phase, a dual geometric constraint strategy is introduced by combining the 2D and 3D spatial geometric information, reducing frequent identity switch errors. Experiments on the AV16.3 and CAV3D datasets show that AVCLNet outperforms state-of-the-art methods, demonstrating superior robustness in multispeaker scenarios.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"238-255"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147618143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Neural-Model-Based Predictive Control for Autonomous Wheel-Legged Robot System","authors":"Jiehao Li,&nbsp;Junzheng Wang,&nbsp;Hongbo Gao,&nbsp;Xiwen Luo,&nbsp;C. L. Philip Chen","doi":"10.1049/cit2.70091","DOIUrl":"10.1049/cit2.70091","url":null,"abstract":"<p>Mobile wheel-legged robots exhibiting mobility, stability and reliability have garnered heightened research attention in demanding real-world scenarios, especially in material transport, emergency response and space exploration. The kinematics model merely delineates the geometric relationship of the controlled objective, disregarding force feedback. This study investigates model predictive trajectory tracking control utilising the robot dynamic model (DRMPC) in the context of unpredictable interactions. The predictive tracking controller for the wheel-legged robot is introduced in the context of position tracking. A dynamic approximator is employed to address the uncertain interactions in the tracking process. Ultimately, co-simulation and empirical tests are conducted to demonstrate the efficacy of the devised control methodology, which achieves high precision and dependable robustness. This work can elucidate the technical and practical oversight of autonomous movement in complicated environments and enhance the manoeuverability and flexibility.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"83-97"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deviation-Guided Attention for Semi-Supervised Anomaly Detection With Contrastive Regularisation","authors":"Guanglei Xie,&nbsp;Xiaochang Hu,&nbsp;Yi Sun,&nbsp;Wenzhuo Zhang,&nbsp;Yafeng Bu,&nbsp;Hao Fu,&nbsp;Xin Xu","doi":"10.1049/cit2.70072","DOIUrl":"10.1049/cit2.70072","url":null,"abstract":"<p>Anomaly detection (AD) aims to identify abnormal patterns that deviate from normal behaviour, playing a critical role in applications such as industrial inspection, medical imaging and autonomous driving. However, AD often faces a scarcity of labelled data. To address this challenge, we propose a novel semi-supervised anomaly detection method, DASAD (Deviation-Guided Attention for Semi-Supervised Anomaly Detection), which integrates deviation-guided attention with contrastive regularisation to reduce the unreliability of pseudo-labels. Specifically, a deviation-guided attention mechanism is designed to combine three types of deviations: latent embeddings, residual direction vectors and hierarchical reconstruction errors to capture anomaly specific cues effectively, thereby enhancing the credibility of pseudo-labels for unlabelled samples. Furthermore, a class-asymmetric contrastive loss is constructed to promote compact representations of normal instances while preserving the structural diversity of anomalies. Extensive experiments on 8 benchmark datasets demonstrate that DASAD consistently outperforms state-of-the-art methods and exhibits strong generalisation across 6 anomaly detection domains.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"66-82"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising Wave Energy Plant Location Through Neutrosophic Multi-Criteria Group Decision-Making","authors":"Hafiz Muhammad Athar Farid,&nbsp;Ayesha Razzaq,&nbsp;Muhammad Riaz,&nbsp;Tapan Senapati,&nbsp;Sarbast Moslem","doi":"10.1049/cit2.70058","DOIUrl":"10.1049/cit2.70058","url":null,"abstract":"<p>The global shift towards sustainable energy has intensified research into renewable sources, particularly wave energy. Pakistan, with its long coastline, holds significant potential for wave energy development. However, identifying optimal locations for wave energy plants involves evaluating complex, multi-faceted criteria. This study employs a multi-criteria group decision-making (MCGDM) approach using single-valued neutrosophic numbers (SVNNs) to address both qualitative and quantitative uncertainties inherent in real-world scenarios. To enhance decision quality, we introduce two novel operators: the single-valued neutrosophic prioritised averaging (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>SVNPA</mtext>\u0000 <mi>d</mi>\u0000 </msub>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> ${text{SVNPA}}_{d}$</annotation>\u0000 </semantics></math>) operator and the single-valued neutrosophic prioritised geometric (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>SVNPG</mtext>\u0000 <mi>d</mi>\u0000 </msub>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> ${text{SVNPG}}_{d}$</annotation>\u0000 </semantics></math>) operator, both incorporating priority degrees. These tools allow decision-makers to express preferences better and handle ambiguous data. The proposed model is validated through comparative analysis with prior studies and demonstrates improved robustness in site selection. Furthermore, we analyse how variations in priority degrees influence decision outcomes, enabling a more dynamic and tailored decision-making process. Our method contributes a more holistic and adaptive framework for selecting locations for wave energy projects, ultimately supporting informed investments in renewable energy infrastructure and improving energy access in underserved coastal regions.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 1","pages":"167-189"},"PeriodicalIF":7.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 2","pages":"367-384"},"PeriodicalIF":7.3,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 2","pages":"548-563"},"PeriodicalIF":7.3,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148081826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 2","pages":"447-463"},"PeriodicalIF":7.3,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148078690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 2","pages":"332-348"},"PeriodicalIF":7.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148075966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 2","pages":"578-591"},"PeriodicalIF":7.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148066173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}