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Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

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arXiv 2024年

作者： Deng, Songhe Zhuo, Wei Xie, Jinheng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model’s ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt Engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://***/CVI-SZU/QA-CLIMS Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

Biogas Fermentation Monitoring Research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning 13

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Biogas Fermentation Monitoring Research Based on Borderline-...

13th International Conference of Information and Communication Technology, ICTech 2024

作者： Xiu, Pengfei Fu, Lijun Sun, Yukun Wang, Hongliang Li, Xuerao Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model. ©2024 IEEE.

关键词： Decision making

SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization

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arXiv 2024年

作者： Yuan, Zhenlong Cao, Jiakai Li, Zhaoxin Jiang, Hao Wang, Zhaoqi Institute of Computing Technology Chinese Academy of Sciences China Agricultural Information Institute Chinese Academy of Agricultural Sciences China Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs China

In this paper, we introduce Segmentation-Driven Deformation Multi-View Stereo (SD-MVS), a method that can effectively tackle challenges in 3D reconstruction of textureless areas. We are the first to adopt the Segment Anything Model (SAM) to distinguish semantic instances in scenes and further leverage these constraints for pixelwise patch deformation on both matching cost and propagation. Concurrently, we propose a unique refinement strategy that combines spherical coordinates and gradient descent on normals and pixelwise search interval on depths, significantly improving the completeness of reconstructed 3D model. Furthermore, we adopt the Expectation-Maximization (EM) algorithm to alternately optimize the aggregate matching cost and hyperparameters, effectively mitigating the problem of parameters being excessively dependent on empirical tuning. Evaluations on the ETH3D high-resolution multi-view stereo benchmark and the Tanks and Temples dataset demonstrate that our method can achieve state-of-the-art results with less time consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

Co-training partial domain adaptation networks for industrial Fault Diagnosis

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arXiv 2024年

作者： Chen, Gecheng Luo, Chengwen Li, Jianqiang Chen, Xinkai The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China The Department of Electronic Information Systems Shibaura Institute of Technology Saitama337-8570 Japan

The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Current PDA approaches primarily rely on adversarial learning for domain adaptation and use reweighting strategies to exclude source samples deemed outliers. However, the transferability of features diminishes from general feature extraction layers to higher task-specific layers in adversarial learning-based adaptation modules, leading to significant negative transfer in PDA settings. We term this issue the adaptation-discrimination paradox (ADP). Furthermore, reweighting strategies often suffer from unreliable pseudo-labels, compromising their effectiveness. Drawing inspiration from traditional classification settings where such partial challenge is not a concern, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to provide a new perspective for the PDA challenge. Each domain-wise model is equipped with a residual domain adaptation (RDA) block to mitigate the ADP problem. Additionally, we introduce a confident information flow via an interactive learning strategy, training the modules of IRDAN sequentially to avoid cross-interference. We also establish a reliable stopping criterion for selecting the best-performing model, ensuring practical usability in real-world applications. Experiments have demonstrated the superior performance of the proposed IRDAN. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Research on intelligent penetration mechanism of netgap data based on database adaptive matching 3

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Research on intelligent penetration mechanism of netgap data...

3rd International Conference on Electronic Information Engineering, big data, and Computer Technology, EIBDCT 2024

作者： Wu, Rutao Xu, Zhiwei Liu, Xuyang Liu, Wenjing Liu, Limin School of Data Science and Application Inner Mongolia University of Technology Huhehaote010000 China Inner Mongolia Autonomous Reg. Software Eng. Serv. Eng. Technology Research Center Based on Big Data Huhehaote010000 China Inner Mongolia Autonomous Region Product Quality Inspection and Research Institute Huhehaote010070 China Haihe Laboratory of Information Technology Application Innovation Tianjin300450 China Institute of Computing Technology of the Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9781510680449

The purpose of data penetration is to achieve timely exchange of data information while ensuring the security of network isolation environment. The current research mainly focuses on protecting data privacy and developing basic data penetration methods for netgap, but these methods generally overlook the adaptive matching of data. Therefore, this article proposes an intelligent netgap data penetration mechanism based on database adaptive matching, which is an end-to-end cross netgap data packet adaptation mechanism. To overcome the problems of overfitting and local optima, XGBoost based on adaptive particle swarm optimization is adopted. By increasing the diversity of subgroups, the adaptive matching effect was improved, and the optimized XGBoost model was fitted to real-time data, with an accuracy increase from 72% to 94%. The experimental results show that this mechanism significantly improves the efficiency of packet adaptive matching while solving the problem of data penetration in the isolated environment of the network gate. The time cost is only 50% of the previous one, which improves the throughput of the system. The optimized XGBoost model is superior to the original model in terms of error and accuracy, and has certain practicality and easy generalization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： data privacy

SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis

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arXiv 2025年

作者： Bai, Runci Xu, Guibao Shi, Yanze Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing100080 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China

Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning. We examine the effects of different attention mechanisms with YOLOv9 for brain tumor detection using the Br35H dataset and our custom dataset (Brain_Tumor_dataset). Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Health risks

A Review of Multimedia Video Services Based on Serverless Cloud computing

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A Review of Multimedia Video Services Based on Serverless Cl...

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Yue Su Liu Sang Weibo Zhao Wei Li Changqing Yuan Cloud Computing & Big Data Research Institute China Academy of Information and Communications Technology Beijing China Academy of Telecommunications Technology Beijing China

Cloud computing technology has achieved rapid growth in recent years. Serverless cloud computing has been applied in more and more scenarios due to its pay-as-you-go, event-driven, and flexible features. This article introduces the application of serverless cloud computing technology in multimedia video services, analyzes the key capabilities, and looks forward to the key development directions in the future.

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Enhanced Semantic Head for Cascade Instance Segmentation

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Enhanced Semantic Head for Cascade Instance Segmentation

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Huang, Xuerong Su, Li Li, Guorong Zhang, Xinfeng Qing, Laiyun Huang, Qingming University of Chinese Academy of Sciences Beijing China Cas Key Lab of Big Data Mining and Knowledge Management Beijing China Institute of Computing Technology Cas Key Lab of Intelligent Information Processing Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recently, cascade instance segmentation inspired by cascade object detection has achieved notable performance. Due to the lack of global information, many methods suffer from incomplete segmentation such as missing edge regions and discontinuities within instances. To solve this problem, we proposed an effective and flexible semantic head to extract enhanced spatial context information. A vision transformer is utilized to generate global context features, and a convolution network is adopted to generate spatial context features. After combining the two modules, we obtain enhanced semantic segmentation features for segmentation. Extensive experiments show that the enhanced semantic head achieves 40.6% and 42.3% mask AP for cascade predictor HTC and DSC, which surpass about 0.9 and 1.4 percentage points respectively. The enhanced semantic head is universal and effective to improve the performance of different cascade predictors. © 2022 IEEE.

关键词： Object detection

The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

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The Power of Bamboo: On the Post-Compromise Security for Sea...

30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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