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Advancing Visible-Infrared Person Re-Identification: Synergizing Visual-Textual Reasoning and Cross-Modal Feature Alignment

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ieee TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2184-2196页

作者： Qiu, Yuxuan Wang, Liyang Song, Wei Liu, Jiawei Shi, Zhiping Jiang, Na Capital Normal Univ Informat Engn Coll Beijing 100048 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

Visible-infrared person re-identification (VI-ReID) is a critical cross-modality fine-grained classification task with significant implications for public safety and security applications. Existing VI-ReID methods primarily focus on extracting modality-invariant features for person retrieval. However, due to the inherent lack of texture information in infrared images, these modality-invariant features tend to emphasize global contexts. Consequently, individuals with similar silhouettes are often misidentified, posing potential risks to security systems and forensic investigations. To address this problem, this paper innovatively introduces natural language descriptions to learn the global-local contexts for VI-ReID. Specifically, we design a framework that jointly optimizes visible-infrared alignment plus (VIAP) and visual-textual reasoning (VTR), and introduces local-global joint measure (LJM) to enhance the metric, while proposing a human-LLM collaborative approach to incorporate textual descriptions into existing cross-modal person re-identification datasets. VIAP achieves cross-modal alignment between RGB and IR. It can explicitly utilize designed frequency-aware modality alignment and relationship-reinforced fusion to explore the potential of local cues in global features and modality-invariant information. VTR proposes pooling selection and dual-level reasoning mechanisms to force the image encoder to pay attention to significant regions based on textual descriptions. LJM proposes introducing local feature distances into the measure stage metric to enhance the relevance of matching using fine-grained information. Extensive experimental results on the popular SYSU-MM01 and RegDB datasets show that the proposed method significantly outperforms state-of-the-art approaches. The dataset is publicly available at https://***/qyx596/vireid-caption.

关键词： Feature extraction Natural languages Cognition Measurement data mining Video recording Identification of persons visualization Semantics Pedestrians Person re-identification modality alignment relation reasoning

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Descriptive Analytics Analysis as Support for Ancestral Domain Preservation: Empirical Evidence Generation as support for Decision Making

Descriptive Analytics Analysis as Support for Ancestral Doma...

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2023 ieee International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023

作者： Castro, Eltimar T. Romano, Nephi P. Odchigue, Rev. F.R. Randy Jasper C. Young, Rev. F.R. John Christian U. Alfante, Jannina De Jesus Valencia, Rochelle A. Father Saturnino Urios University Data Science Analytics Center Butuan City Philippines Father Saturnino Urios University Office of VP Academic Affairs and Research Butuan City Philippines Father Saturnino Urios University Office of the President Dept. Butuan City Philippines Father Saturnino Urios University Arts and Science Program Butuan City Philippines

ISBN: (纸本)9798350321302

Ancestral domain refers to the lands, territories, and resources possessed and administered collectively by indigenous peoples, such as the indigenous communities of the Philippines. Indigenous peoples who have inhabited and cared for these regions for generations attach great cultural, spiritual, and economic significance to them. Indigenous Peoples Rights Act (IPRA) of 1997 acknowledged for the first time the idea of ancestral domain in the Philippines. However, the IPRA's implementation and the preservation of ancestral properties in the Philippines have encountered obstacles. There have been instances of unlawful logging, mining, and land appropriation in ancestral domains, typically with the participation of local government officials and influential persons. This has resulted in the uprooting of indigenous populations and the devastation of their land and resources. This research aimed to analyze the data in relation to tree planting activities initiated by several advocates, especially the Father Saturnino Urios University Foundation. Which aims, helping to preserve and saving Ancestral Land. This research employs data mining activity and develops insights according to generated patterns. Descriptive analytics is the branch of data analysis that involves summarizing and describing data. It typically involves using statistics, data visualization, and other techniques to identify patterns and trends in data. Descriptive analytics can be used to understand the characteristics of data and to communicate those characteristics to others. Further, the results show that tree planting activities conducted by some advocates resulted in good results, since planted trees near the watershed have had a survival rate of 92.28 (%) percent and around 43,270 seedlings planted in 88.05 hectares. On the other hand, abaca have been planted in the area for the livelihood of IP situated in the area, have a survival rate of 96 (%) percent. This result is the output of an ongoing reh

关键词： data visualization

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ScGDCC: Graph-based Dual Contrastive Calibration for Single Cell MultiOmics Clustering

ScGDCC: Graph-based Dual Contrastive Calibration for Single ...

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2024 ieee International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tao, Huayu Sun, Xinliang Chen, Yuxuan Li, Min Zheng, Ruiqing Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Cell clustering is vital for studying cellular heterogeneity and understanding biological mechanisms. With the advancement of sequencing technologies, it is now possible to obtain multiomics data from single cells, such as ATAC-seq and RNA-seq. Compared to single-omics data, multiomics data offer a more comprehensive view of the cellular landscape. Although several single-cell multiomics clustering methods have been developed, the sparsity and complexity of multiomics data make clustering a challenging computational task. This paper proposes a single cell multiomics clustering method called scGDCC, which is based on graph neural networks and dual contrastive calibration. scGDCC utilizes graph neural networks to capture the neighborhood information of cells and employs dual contrastive calibration to achieve more consistent joint representations of cells. Experiments on five dual-omics datasets (ATAC-seq and RNA-seq) and two triple-omics datasets (ATAC-seq, RNA-seq, and protein) demonstrate the superiority of this clustering method. Additionally, visualization experiments further validate the effectiveness of the joint cellular representations. © 2024 ieee.

关键词： Contrastive Learning

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IoT Based Smart Plant Monitoring System 13

IoT Based Smart Plant Monitoring System

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13th International Conference on Cloud Computing, data science and Engineering, Confluence 2023

作者： Nehra, Vibha Sharma, Megha Sharma, Vaibhav Amity University Department of Computer Science and Engineering Uttar Pradesh India Amity University Amity School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9781665462631

Agriculture is a vast area that needs to be studied. Technology development has brought us to the situation where there are sophisticated agricultural tools and systems. About 80% of India's surface water is used for agriculture. Applying IoT becomes necessary to ensure proper use of water resources and high-quality output. Because optimal irrigation in fields can provide better crops, this paper presents an open-source technology-based smart system to predict irrigation requirements of a field using sensing of ground parameters like soil moisture, environmental conditions like temperature and humidity, and external parameters like obstacle detection or intruder alert using an infrared sensor. The proposed system is based on an algorithm that takes into account sensed data in addition to the previously mentioned parameters. The entire system has been created and deployed on a pilot scale, and it wirelessly collects sensor node data over the cloud using web services, while a both web and mobile based information visualization and decision support system offers real-time insights based on the analysis of the sensor. The analysis displayed clear differences between the monitored and unmonitored environment and how it affects the plant health in both short and long term. The graphs help in visualizing the differences between the environment at fixed time intervals. In the future, this system can be combined with other technologies to predict rainfall, manage pH levels, and secure the vast amounts of data created and saved over the cloud by sensors. This would improve the system's long-term viability and make it more independent of human operation. © 2023 ieee.

关键词： Soil moisture

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M2LADS: A System for Generating MultiModal Learning Analytics Dashboards 47

M2LADS: A System for Generating MultiModal Learning Analytic...

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47th ieee-Computer-Society Annual International Conference on Computers, Software, and Applications (COMPSAC)

作者： Becerra, Alvaro Daza, Roberto Cobos, Ruth Morales, Aythami Cukurova, Mutlu Fierrez, Julian Univ Autonoma Madrid Sch Engn Madrid Spain UCL London England

ISBN: (纸本)9798350326970

In this article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in learning sessions in a MOOC in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures, visual attention from the video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGCE and MOOC tracking logs respectively, and both are included in the Web-based System. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the MOOC, which can in turn be used to improve their learning outcomes through feedback visualizations and interventions, as well as to enhance learning analytics models and improve the open content of the MOOC.

关键词： Open Education MOOC e-Learning Biometrics and Behavior Multimodal Learning Analytics Web-based Technology Computer science & Information Technology

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FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference

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ieee TRANSACTIONS ON BROADCASTING 2023年第1期69卷 10-20页

作者： Wei, Qinglan Huang, Xuling Zhang, Yuan Commun Univ China Beijing 100000 Peoples R China

In the latest social networks, more and more people prefer to express their emotions in videos through text, speech, and rich facial expressions. Multimodal video emotion analysis techniques can help understand users' inner world automatically based on human expressions and gestures in images, tones in voices, and recognized natural language. However, in the existing research, the acoustic modality has long been in a marginal position as compared to visual and textual modalities. That is, it tends to be more difficult to improve the contribution of the acoustic modality for the whole multimodal emotion recognition task. Besides, although better performance can be obtained by introducing common deep learning methods, the complex structures of these training models always result in low inference efficiency, especially when exposed to high-resolution and long-length videos. Moreover, the lack of a fully end-to-end multimodal video emotion recognition system hinders its application. In this paper, we designed a fully multimodal video-to-emotion system (named FV2ES) for fast yet effective recognition inference, whose benefits are threefold: (1) The adoption of the hierarchical attention method upon the sound spectra breaks through the limited contribution of the acoustic modality and outperforms the existing models' performance on both IEMOCAP and CMU-MOSEI datasets;(2) the introduction of the idea of multi-scale for visual extraction while single-branch for inference brings higher efficiency and maintains the prediction accuracy at the same time;(3) the further integration of data pre-processing into the aligned multimodal learning model allows the significant reduction of computational costs and storage space.

关键词： Emotion recognition Feature extraction Acoustics visualization Transformers Deep learning data mining Multimodal emotion inference fully end-to-end

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MedFILIP: Medical Fine-Grained Language-Image Pre-Training

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ieee JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第5期29卷 3587-3597页

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Harbin Med Univ Affiliated Hosp 4 Dept Thorac Surg Harbin 150088 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518071 Peoples R China Northeast Forestry Univ Coll Comp & Control Engn Harbin 150040 Peoples R China King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 23955 Saudi Arabia Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigdata, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%.

关键词： Diseases Medical diagnostic imaging visualization Contrastive learning Feature extraction data mining Training Large language models Complexity theory Bioinformatics CXR imaging fine-grained interpretability vision-language pretraining

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Indexing and visualization of Climate Change Narratives Using BERT and Causal Extraction

Indexing and Visualization of Climate Change Narratives Usin...

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2023 ieee International Conference on Big data, Bigdata 2023

作者： Sakaji, Hiroki Kaneda, Noriyasu Hokkaido University Research Faculty of Information Science and Technology Japan Bank of Japan Japan

ISBN: (纸本)9798350324457

In this study, we propose a methodology to extract, index, and visualize 'climate change narratives' (stories about the connection between causal and consequential events related to climate change). We use two natural language processing methods, BERT (Bidirectional Encoder Representations from Transformers) and causal extraction, to textually analyze newspaper articles on climate change to extract 'climate change narratives.' The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers. Looking at the extracted climate change narratives over time, we find that since 2018, an increasing number of narratives suggest the impact of the development of climate change policy discussion and the implementation of climate change-related policies on corporate behaviors, macroeconomics, and price dynamics. We also observed the recent emergence of narratives focusing on the linkages between climate change-related policies and monetary policy. Furthermore, there is a growing awareness of the negative impacts of natural disasters (e.g., abnormal weather and severe floods) related to climate change on economic activities, and this issue might be perceived as a new challenge for companies and governments. The methodology of this study is expected to be applied to a wide range of fields, as it can analyze causal relationships among various economic topics, including analysis of inflation expectation or monetary policy communication strategy. © 2023 ieee.

关键词： Causal Chain Climate Change Narrative Economics

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CANet: A Spatial Structure Constraint and Local Semantic Awareness Based Network for Weakly Supervised Building Extraction

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ieee TRANSACTIONS ON GEOscience AND REMOTE SENSING 2025年 63卷

作者： Wang, Siyuan Shao, Zhenfeng Hou, Dongyang Wang, Yu Wang, Jinyang Cai, Bowen Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Cent South Univ Sch Geosci & Infophys Changsha 410000 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

Benefitting from the easy availability of image-level labels, weakly supervised semantic segmentation (WSSS) methods based on class activation maps (CAMs) have made significant progress in building extraction from remote sensing imagery. However, image-level labels lack precise spatial locations and boundary ranges of buildings, posing challenges in achieving comprehensive and structurally clear building extraction. Furthermore, due to the complex background interference and the diversity of building in high-resolution remote sensing imagery (HRRS), small and sparse buildings suffer from insufficient attention in CAMs. To solve the above problems, this article proposes a spatial structure constraint and local semantic awareness-based WSSS method, CANet, for extracting buildings from HRRS. Specifically, we design a spatial structure constraint module to generate CAMs with finer spatial structural details of buildings, which minimizes feature differences from patches of different granularities and the whole image. Moreover, a local semantic awareness module is designed to address the issue of insufficient coverage of CAMs on sparse and tiny building. This module first strengthens the feature extraction network by embedding discriminative suppression units to force the network to focus on more nondiscriminative regions. Subsequently, visual word learning is introduced to identify additional object categories. Finally, four WSSS datasets are constructed based on public datasets with two simple and two complex scenarios. The results demonstrate that the proposed method outperforms 11 state-of-the-art methods, improving the intersection over union by at least 3.46% and 0.29% in both simple and complex scenarios, respectively.

关键词： Cams Buildings Semantics Remote sensing Feature extraction data mining Accuracy Semantic segmentation Conditional random fields visualization Building extraction remote sensing self-supervised learning (SSL) weakly supervised deep learning

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A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第2期36卷 2118-2132页

作者： Wang, Ke Zhu, Mingjia Chen, Zicong Weng, Jian Li, Ming Yiu, Siu-Ming Ding, Weiping Gu, Tianlong Jinan Univ Minist Educ Engn Res Ctr Trustworthy AI Guangzhou 510632 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Jinan Univ Coll Informat & Sci Guangzhou 510632 Peoples R China Jinan Univ Natl Joint Engn Res Ctr Network Secur Detect & Pr Guangzhou 510632 Peoples R China Jinan Univ Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou 510632 Peoples R China Jinan Univ Guangdong Hong Kong Joint Lab Data Secur & Privac Guangzhou 510632 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Nantong Univ Sch Informat & Sci Nantong 226019 Peoples R China

The adversarial vulnerability of convolutional neural networks (CNNs) refers to the performance degradation of CNNs under adversarial attacks, leading to incorrect decisions. However, the causes of adversarial vulnerability in CNNs remain unknown. To address this issue, we propose a unique cross-scale analytical approach from a statistical physics perspective. It reveals that the huge amount of nonlinear effects inherent in CNNs is the fundamental cause for the formation and evolution of system vulnerability. Vulnerability is spontaneously formed on the macroscopic level after the symmetry of the system is broken through the nonlinear interaction between microscopic state order parameters. We develop a cascade failure algorithm, visualizing how micro perturbations on neurons' activation can cascade and influence macro decision paths. Our empirical results demonstrate the interplay between microlevel activation maps and macrolevel decision-making and provide a statistical physics perspective to understand the causality behind CNN vulnerability. Our work will help subsequent research to improve the adversarial robustness of CNNs.

关键词： visualization Decision making Physics Neurons Neural networks Convolutional neural networks Mathematical models Adversarial vulnerability cascading failure causality convolutional neural network (CNN) statistical physics

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