检索结果-内蒙古大学图书馆

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jueqi Wei Yuanwu Xu Mohan Chen Yuejie Zhang Rui Feng Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Technology Deakin University

Temporal sentence grounding aims to detect the target segment most related to a given query in an untrimmed video. To alleviate the expensive annotation cost for temporal labels, researchers paid more attention to weakly supervised setting. Prior studies neglected the utilization of video representation reconstruction, which led to an unbalanced alignment learning. Moreover, they used different strategies to generate proposals which ignored the temporal structure in a query. In this paper, we propose a novel Conditional Video-Text Reconstruction Network (CVTRN). It supports conditional reconstruction of video and text representation. Specifically, video and text features are fused to compute semantic alignment, which is the condition of reconstruction. A new mask strategy for mask conditioned sentence reconstruction is also devised. This strategy focuses more on boundary regions than the widely used Gaussian mask in previous methods. Experimental results on two public benchmark datasets show that our CVTRN outperforms the state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SPTNET: Span-based Prompt Tuning for Video Grounding

SPTNET: Span-based Prompt Tuning for Video Grounding

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiren Zhang Yuanwu Xu Mohan Chen Yuejie Zhang Rui Feng Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Technology Deakin University

When a Pre-trained Language Model (PLM) is adopted in video grounding task, it usually acts as a text encoder without having its knowledge fully utilized. Also, there exists an inconsistency problem between the pre-training and downstream objectives. To solve the issues, we propose a new paradigm, named Span-based Prompt Tuning (SPTNet). It can convert the video grounding task into a cloze form. Specifically, a query is first changed into a form with mask token by a template, then the video and the query embeddings are integrated through a cross-modal transformer. The start and end points of the query matching time span are predicted with the embedding of the mask token. Experimental results on two public benchmarks ActivityNet Captions and Charades-STA show that our SPTNet achieves surpassing performance compared with state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence-based pathological analysis of liver cancer:Current advancements and interpretative strategies

引用

iLIVER 2024年第1期3卷 82-89页

作者： Guang-Yu Ding Jie-Yi Shi Xiao-Dong Wang Bo Yan Xi-Yang Liu Qiang Gao Department of Liver Surgery and Transplantation Liver Cancer InstituteZhongshan HospitalShanghai 200032China Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education Fudan UniversityShanghai 200032China School of Computer Science and Technology Xidian UniversityXi'an 710126China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Institute of Biomedical Sciences Fudan UniversityShanghai 200030China State Key Laboratory of Genetic Engineering Fudan UniversityShanghai 200433China

In recent years,significant advances have been achieved in liver cancer management with the development of artificial intelligence(AI).AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular ***,AI techniques have a“black box”nature,which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical *** this paper,we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer,and delve into the strategies used in recent studies to unravel the“black box”of AI's decision-making process.

关键词： Liver cancer Artificial intelligence Pathology Interpretative model

来源：评论

学校读者我要写书评

暂无评论

A Detection Method Toward Yellow Dragon Disease Spreads Using Spark and Deep Learning 3

A Detection Method Toward Yellow Dragon Disease Spreads Usin...

引用

3rd International Conference on High Performance computing and Communication Engineering, HPCCE 2023

作者： Xie, Wu Su, Zhenzhao Zhang, Huimin Kang, Ping Qin, Kun Fan, Yong Zhao, Quanyou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541214 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Lingchuan Science and Technology Information Institute Guilin541299 China Guilin Hivision Technology Co. Ltd. Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541214 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before. © 2024 SPIE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

BayLing 2: A Multilingual Large Language Model with Efficient Language Alignment

arXiv

引用

arXiv 2024年

作者： Zhang, Shaolei Zhang, Kehao Fang, Qingkai Guo, Shoutao Zhou, Yan Liu, Xiaodong Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences ICT CAS China Key Laboratory of AI Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China Research Center of Distributed Systems Institute of Computing Technology Chinese Academy of Sciences ICT CAS China

Large language models (LLMs), with their powerful generative capabilities and vast knowledge, empower various tasks in everyday life. However, these abilities are primarily concentrated in high-resource languages, leaving low-resource languages with weaker generative capabilities and relatively limited knowledge. Enhancing the multilingual capabilities of LLMs is therefore crucial for serving over 100 linguistic communities worldwide. An intuitive approach to enhance the multilingual capabilities would be to construct instruction data for various languages, but constructing instruction data for over 100 languages is prohibitively costly. In this paper, we introduce BayLing 2, which efficiently transfers generative capabilities and knowledge from high-resource languages to low-resource languages through language alignment. To achieve this, we constructed a dataset of 3.2 million instructions, comprising high-resource language instructions (Chinese and English) and cross-lingual instructions for 100+ languages and performed instruction tuning based on the dataset to facilitate the capability transfer between languages. Using Llama as the foundation model, we developed BayLing-2-7B, BayLing-2-13B, and BayLing-2-8B, and conducted a comprehensive evaluation of BayLing. For multilingual translation across 100+ languages, BayLing shows superior performance compared to open-source models of similar scale. For multilingual knowledge and understanding benchmarks, BayLing achieves significant improvements across over 20 low-resource languages, demonstrating its capability of effective knowledge transfer from high-resource to low-resource languages. Furthermore, results on English benchmarks indicate that BayLing maintains high performance in high-resource languages while enhancing the performance in low-resource languages. Demo2, homepage3, code4 and models5of BayLing are available. © 2024, CC BY-NC-ND.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Language-Image Pre-Training 24

FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Lang...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Yudong Hou, Xianxu Dezhi, Zheng Shen, Linlin Zhao, Zhe School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Tencent AI Lab Beijing China

ISBN: (纸本)9798400706868

While significant progress has been made in multi-modal learning driven by large-scale image-text datasets, there is still a noticeable gap in the availability of such datasets within the facial domain. To facilitate and advance the field of facial representation learning, we present FLIP-80M, a large-scale visual-linguistic dataset comprising over 80 million face images paired with text descriptions. FLIP-80M is constructed by leveraging the large openly available image-text-pair dataset LAION-5B and a mixed-method approach to filter face-related pairs from both visual and linguistic perspectives. Our curation process involves face detection, face caption classification, text de-noising, and synthesis-based image augmentation. As a result, FLIP-80M stands as the largest face-text dataset to date. To evaluate the potential of our dataset, we fine-tune the CLIP model using the proposed FLIP-80M, to create FLIP (Facial Language-Image Pretraining) and assess its representation capabilities across various downstream tasks. Our experiments demonstrate that our FLIP model achieves state-of-the-art results in a range of face analysis tasks, including face parsing, face alignment, and face attribute classification. The dataset and models are available at https://***/ydli-ai/FLIP. © 2024 ACM.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

A Deep Ship Recognition Model Based on Multi-category Fusion of General Datasets 6

A Deep Ship Recognition Model Based on Multi-category Fusion...

引用

6th IEEE International Conference on Civil Aviation Safety and information technology, ICCASIT 2024

作者： Su, Hang Qiu, Guoping Dongguan University of Technology School of Telecommunications Engineering and Intelligentization Guangdong Dongguan523808 China College of Electronic and Information Engineering Shenzhen University Guangdong Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen518060 China Shenzhen University Branch Shenzhen Institute of Artificial Intelligence and Robotics Guangdong Shenzhen518060 China Shenzhen University Shenzhen Key Laboratory of Digital Creative Technology Guangdong Shenzhen518060 China

ISBN: (纸本)9798350389418

To meet the demand for accurate and rapid recognition of ship targets in existing ship image recognition methods, this study applies the DCNN model, known for its powerful feature extraction capabilities, to the task of ship image recognition. To enhance the model's performance, a novel dataset fusion training method is utilized. The DCNN model requires large-scale labeled training datasets;however, there is currently a lack of diverse and large-scale labeled ship image datasets in the public domain. Additionally, manually collecting and labeling large-scale ship recognition data is labor-intensive. Therefore, this paper proposes a new approach by creating a ship target recognition dataset that fuses existing general datasets for training the ship recognition model. Furthermore, a model training method that integrates multi-category image recognition is proposed. In this method, a focal loss-based optimization training approach is specifically employed to handle imbalanced data classification, thereby improving the recognition performance of ship images within multi-category datasets. Performance tests on the trained model show that the mean Average Precision (mAP) of the model trained with multi-category fusion is relatively improved by 9.45 % compared to the 3 8. 1 9 % mAP of the model trained without focal loss on a single category. Experimental results demonstrate that the proposed multi-category image training method can effectively enhance the performance of ship image recognition tasks in training using existing general image recognition datasets. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An Improved Method for Long-term Drift Compensation of Electronic Nose with Batch Control 12

An Improved Method for Long-term Drift Compensation of Elect...

引用

12th IEEE International Conference on Electronics information and Emergency Communication, ICEIEC 2022

作者： Cui, Hao Dong, Xiaorui Shang, Kai Shandong Institute of Petroleum and Chemical Technology Key Laboratory of Intelligent Information Processing Shandong Province Dongying China College of Computer Science and Technology China University of Petroleum Shandong Province Qingdao China

ISBN: (数字)9781665407540

ISBN: (纸本)9781665407540

Sensor long-time drift compensation has always been a huge and important research challenge. To address this problem, this paper proposes a simple and efficient long-time drift compensation method based on a short-time memory-forgetting mechanism. The proposed improved method controls the update frequency of memory and forgetting by introducing a batch control coefficient to achieve a balance between the update frequency of memory queue and model iteration. In this paper, we use the gas sensor array drift dataset as the research object and design two experimental strategies to verify the processing time compression degree and the accuracy change of the classification model by adjusting the batch control coefficient. The experiments prove that the proposed method in this paper has an excellent effect on compressing data processing time and relieving hardware pressure, which has a certain reference value for related research. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

引用

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei 230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei 230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3WD Depression Detection Multi-View Feature Fusion Social Media Texts

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：