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SIEVL-Track: Exploring Semantic Information Enhancement for Visual-Language Object Tracking

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Ning Zhong, Bineng Liang, Qihua Mo, Zhiyi Nong, Jian Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China

With the assistance of language descriptions, Visual-Language (VL) object tracking can obtain more accurate semantic information compared to traditional Visual-Only object tracking. However, the ability of current VL trackers to obtain target semantic information has not been fully developed due to limitations such as wasted modeling capabilities and insufficient utilization of historical temporal information. On the one hand, the modeling output from Transformer shallow encoders often does not directly participate in the prediction of tracking results, resulting in a certain degree of model capability waste. On the other hand, the semantic information of historical tracking results has also not been fully utilized in the tracking process, resulting in a certain degree of lack of semantic assistance capability. Therefore, we propose a novel hierarchical multi-stage VL tracker called SIEVL-Track to enhance target semantic information. Specifically, we first design a multi-stage visual language tracking framework for modeling multi-scale semantic information in Visual-Language tracking pipeline. Secondly, we propose a selective deep and shallow semantic information fusion module (S-DSFM) that explicitly integrates shallow output features into deep output features, so to reduce the waste of modeling capabilities and obtain more high-frequency semantic information related to the target. Finally, we design a temporal cue modeling module based on linguistic classification and multi-frame historical information(MHLS-TCM), with the aim of more comprehensive utilization of historical temporal semantic information. Benefit from the above designs, our VL tracker can obtain stronger target semantic information. Competitive performance from extensive experimental results on five popular vision-language tracking benchmarks, including LaSOT, OTB99-Lang, WebUAV-3M, LaSOText and TNL2K, have demonstrated the superiority and effectiveness of our SIEVL-Track. © 1991-2012 IEEE.

关键词： Semantics

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DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation

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Computers, Materials & Continua 2025年第3期82卷 4841-4862页

作者： Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China

The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels requires further *** methods face challenges in efficiently managing multi-scale fea-tures of different granularities from the encoder backbone,leaving room for improvement in their global representation and feature extraction *** address these challenges,we propose a novel Decoder with Multi-Head Feature Receptors(DMHFR),which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities:coarse,fine-grained,and full *** groups are subsequently processed by Multi-Head Feature Receptors(MHFRs)after feature capture and modeling *** include two Three-Head Feature Receptors(THFRs)and one Four-Head Feature Receptor(FHFR).Each group of features is passed through these MHFRs and then fed into axial transformers,which help the model capture long-range dependencies within the *** three MHFRs produce three distinct feature *** output from the FHFR serves as auxiliary auxiliary features in the prediction head,and the prediction output and their losses will eventually be *** results show that the Transformer using DMHFR outperforms 15 state of the arts(SOTA)methods on five public ***,it achieved significant improvements in mean DICE scores over the classic Parallel Reverse Attention Network(PraNet)method,with gains of 4.1%,2.2%,1.4%,8.9%,and 16.3%on the CVC-ClinicDB,Kvasir-SEG,CVC-T,CVC-ColonDB,and ETIS-LaribPolypDB datasets,respectively.

关键词： Medical image segmentation feature exploration feature aggregation deep learning multi-head feature receptor

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Uncertainty-Guided Diffusion Model for Camouflaged Object Detection

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IEEE Transactions on Multimedia 2025年

作者： Yang, Jinsheng Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Song, Shuxiang Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China

Recently, diffusion models have significantly improved the performance of Camouflaged Object Detection (COD) by adding noise to a mask and iteratively denoising it to match the target distributions. Due to the direct extraction of features from noisy masks and the lack of conditional constraints on a prediction area, the diffusion model may deviate from a correct prediction range and produces mispredictions in regions with high uncertainty. To address this issue, we propose an uncertainty-guided diffusion model (UGDNet) for COD, which explicitly quantifies uncertainty and integrates it as an anchor condition into the diffusion models to provide an initialization of the diffusion regions. The core idea is first to utilize a probability representation and transformer to explicitly model uncertainty, aiming to identify areas where a model may generate overconfident mispredictions. Then, we use the uncertainty as an anchor condition to provide a reference prediction range for the diffusion model, guiding each step of the diffusion process. Furthermore, we use uncertainty to guide feature aggregation, prompting the model to pay extra attention to the semantic features of regions with high uncertainty to refine the segmentation results further. The experimental results indicate that our proposed UGDNet achieves higher accuracy than existing state-of-the-art models on five COD benchmarks, including COD10K, NC4K, CAMO, CHAMELEON, and CDS2K. © 1999-2012 IEEE.

关键词： Semantics

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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A prediction approach based on long short-term memory networks for dynamic multiobjective optimization

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Expert Systems with Applications 2025年 283卷

作者： Xu, Biao Rang, Gejie Xie, Ruijie Li, Wenji Gong, Dunwei Fan, Zhun Yang, Shengxiang He, Jie College of Engineering Shantou University Shantou515063 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China Shenzhen institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Dynamic multiobjective optimization problems (DMOPs) present significant challenges to conventional evolutionary optimization methods because of the continuous changes in their Pareto-optimal sets (PSs) and fronts (PFs). Prediction-driven approaches have demonstrated potential in rapidly adapting to these changes. However, many existing methods depend on linear models to forecast the evolving PSs, which may be restrictive. To counteract this limitation, this research presents a novel dynamic multiobjective evolutionary optimization algorithm that incorporates predictions from long short-term memory (LSTM) networks. Initially, in our methodology, the PS for each problem is segmented into multiple clusters, and the centroid of each cluster is identified. These cluster centroids, representing the PSs across various environmental conditions, are then transformed into a series of time series data. The LSTM network models are subsequently trained on this time series data as input samples. Utilizing these refined models, the centroids of the evolving PSs are predicted with improved precision. Moreover, to enhance the performance of the algorithm, an innovative population-generation strategy is also introduced that guarantees a well-converged and diverse starting population. Our proposed algorithm undergoes rigorous testing using benchmark functions, and the outcomes validate its proficiency in tackling DMOPs, showing superior performance compared to existing state-of-the-art algorithms. © 2025 Elsevier Ltd

关键词： Multiobjective optimization

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Colour Variation Minimization Retinex Decomposition and Enhancement with a Multi-Branch Decomposition Network

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Chinese Journal of Electronics 2023年第4期32卷 908-919页

作者： DENG Jiawei YU Zhenming PANG Guangyao Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University School of Information and Communication Guilin University of Electronic Technology

This paper proposes a colour variation minimization retinex decomposition and enhancement with a multi-branch decomposition network(CvmD-net)to remove single image darkness. The network overcomes the problem that retinex deep learning model relies on matching bright images to process dark images. Specifically, our method takes two stages to light up the darkness in initial images: image decomposition and brightness optimization. We propose an input constant feature prior mechanism(ICFP) based on reflection constant features. The mechanism extracts structure and colour from the input images and constrains the reflected images output from the decomposition model to reduce color distortion and artifacts. The noise amplification during decomposition is addressed by a multi-branch decomposition network. Sub-networks with different structures are employed to focus on different prediction tasks. This paper proposes a reference mechanism for input brightness. This mechanism optimizes the output brightness distribution by calculating the reference brightness of the dark *** results on two benchmark datasets, namely,LOL and ZeroDCE, demonstrate that the proposed method can better balance dense noise interference and colour restoration. For the evaluation on real images, we collect Skynet images at night to verify the performance of the proposed approach. Compared with the state-of-the-art non-reference retinex decomposition-enhancement models,this paper has the best brightness optimization.

关键词： Reflectivity Image color analysis Brightness Interference Feature extraction Minimization Reflection

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Research and implementation of improved license plate segmentation and recognition algorithm 7

Research and implementation of improved license plate segmen...

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7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Liao, Qiuxiang Yang, Qiuhui Wang, Liang Wuzhou University Guangxi China Key Laboratory of Machine Vision and Intelligent Control of Guangxi Province Guangxi China

ISBN: (纸本)9798350334197

License plate recognition system is an important part of information collection in intelligent transportation system, so it is of great practical significance to study it deeply. In this paper, the character segmentation and recognition algorithm of license plate after positioning correction are studied. Aiming at the problems such as the low accuracy of license plate recognition, slow speed of traditional license plate recognition algorithm for complex environment. Aiming at the special license plate or the license plate with stained border, the license plate is divided into seven equal parts by column, and an improved border removal algorithm is proposed, which only deals with the special column of the license plate. In the character segmentation algorithm, the improved projection method is used to realize the accurate segmentation of the disconnected characters. The principle of the algorithm is simple and the processing time is fast. In the character recognition algorithm, the correlation matching method based on Manhattan distance is adopted. The experimental test results show that the algorithm in this paper has improved the accuracy and speed of license plate recognition. © 2023 IEEE.

关键词： License plates (automobile)

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Analysis of the Influence of Domestic Mainstream RTCM Differential Data on RTK Positioning Results 23

Analysis of the Influence of Domestic Mainstream RTCM Differ...

引用

7th International Conference on Computer Science and Application Engineering, CSAE 2023

作者： Chen, Minglang He, Jie Xu, Caixu Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9798400700590

In China, most provinces have established mature Continuous Operational Reference Systems (CORS) base stations, and enterprises such as Qianxun Spatial Intelligence Inc., (hereafter called Qianxun), China Mobile, and Six Points Technology have all deployed CORS sites one after another. Throughout this paper, the problem of differences in the accuracy of differential corrections broadcast by different types of CORS base stations is addressed, affecting Real Time Kinematic (RTK) positioning. As examples, CORS base stations of Qianxun and China Mobile broadcast differential data, and the same type of receivers are used to collect RTCM3 differential data, static raw data of mobile stations, and GGA data with similar baseline distances. After decoding the raw data, the ionospheric delay and tropospheric delay after double-differenced are back-projected through the combination of the ionosphere free, and time series graphs of their different time periods are obtained to compare the differences of RTCM3 differential data broadcast by different types of base stations. As a result of the method presented in this paper, it is possible to assess how ionospheric and tropospheric delay errors, along with other factors, affect real-time positioning accuracy in mapping, unmanned vehicles, and other fields. This is of great benefit for improving real-time positioning accuracy. © 2023 ACM.

关键词： Signal receivers

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Improved Algorithm for License Plate Location Based on Edge Detection and Mathematical Morphology 6

Improved Algorithm for License Plate Location Based on Edge ...

引用

6th IEEE Advanced Information Management, Communicates, Electronic and Automation control Conference, IMCEC 2024

作者： Qiuxiang, Liao Qiuhui, Yang Liang, Wang Wuzhou University Guangxi China Key Laboratory of Machine Vision and Intelligent Control of Guangxi Province Guangxi China

ISBN: (纸本)9798350316520

License plate localization is the crucial first step in the license plate recognition system. In this paper, the weighted average method was used to preprocess the license plate image, and the Roberts operator was used to preserve the contour information of the license plate area and the whole *** mathematical morphology operations with the features of license plate images, using corrosion operations for filtering to achieve rough license plate *** precise location of the license plate area was realized by using the reasonable selection of structural elements in the closed operation and the open *** algorithm reduces the appearance of interference connected regions, thereby improving the speed of license plate *** effectively improves the traditional license plate location method and saves time for the subsequent license plate *** experimental results show that this algorithm can accurately and quickly locate license plate images in different environments, and has certain robustness. © 2024 IEEE.

关键词： Mathematical morphology

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Image Retrieval Algorithm Based on Fractal Coding 6th

Image Retrieval Algorithm Based on Fractal Coding

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6th EAI International Conference on machine Learning and intelligent Communications, MLICOM 2021

作者： Guo, Hui He, Jie Xu, Caixu Li, Dongling Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9783031044083

A traditional fractal image retrieval system needs to code all the images before the retrieval, and thus real-time retrieval cannot be realized. Focusing on this problem, this study puts forward an image retrieval algorithm based on image entropy and fractal blocks. In the algorithm, images are screened at first according to comparison of image entropies. Therefore, the screened images in an image library are similar to a query image to some extent. In this way, the number of images requiring to be matched with the query image in the image library can be reduced. In the meanwhile, the time for image retrieval can be greatly shortened. Then, the retrieval function is realized based on the characteristic computation structure similarity of fractal blocks of images. As shown by the experimental results, the algorithm does not need to extract fractal code documents at first when images are put into the library. Thus, defects in offline retrieval of the traditional fractal image retrieval can be overcome. In addition, a precision ratio and a recall ratio of checking results can be ensured. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Fractals

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