检索结果-内蒙古大学图书馆

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Yuchen Xu, Qianqian Wang, Zitai Yang, Zhiyong He, Junwei Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)157735897X

Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information among classes. To this end, JointEnergy, which is a representative multi-label OOD inference criterion, summarizes the logits of all the classes. However, we find that JointEnergy can produce an imbalance problem in OOD detection, especially when the model lacks enough discrimination ability. Specifically, we find that the samples only related to minority classes tend to be classified as OOD samples due to the ambiguous energy decision boundary. Besides, imbalanced multi-label learning methods, originally designed for ID ones, would not be suitable for OOD detection scenarios, even producing a serious negative transfer effect. In this paper, we resort to auxiliary outlier exposure (OE) and propose an unknown-aware multi-label learning framework to reshape the uncertainty energy space layout. In this framework, the energy score is separately optimized for tail ID samples and unknown samples, and the energy distribution gap between them is expanded, such that the tail ID samples can have a significantly larger energy score than the OOD ones. What's more, a simple yet effective measure is designed to select more informative OE datasets. Finally, comprehensive experimental results on multiple multi-label and OOD datasets reveal the effectiveness of the proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration

arXiv

引用

arXiv 2025年

作者： Li, Renkai Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

Video-based person re-identification (ReID) has become increasingly important due to its applications in video surveillance applications. By employing events in video-based person ReID, more motion information can be provided between continuous frames to improve recognition accuracy. Previous approaches have assisted by introducing event data into the video person ReID task, but they still cannot avoid the privacy leakage problem caused by RGB images. In order to avoid privacy attacks and to take advantage of the benefits of event data, we consider using only event data. To make full use of the information in the event stream, we propose a Cross-Modality and Temporal Collaboration (CMTC) network for event-based video person ReID. First, we design an event transform network to obtain corresponding auxiliary information from the input of raw events. Additionally, we propose a differential modality collaboration module to balance the roles of events and auxiliaries to achieve complementary effects. Furthermore, we introduce a temporal collaboration module to exploit motion information and appearance cues. Experimental results demonstrate that our method outperforms others in the task of event-based video person ReID. Copyright © 2025, The Authors. All rights reserved.

关键词： information leakage

来源：评论

学校读者我要写书评

暂无评论

A High Linearity V-Band Power Amplifier With Gain Response Shaping Method in 65-nm CMOS

A High Linearity V-Band Power Amplifier With Gain Response S...

引用

Cross Strait Quad-Regional Radio Science and Wireless technology Conference (CSRWTC)

作者： Zhenhao Guan Peng Fei Hu Pei Qin Hao Xu Nan Yang Guangdong Provincial Key Laboratoryof Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology (School of Microelectronics) Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Millimeter-Wave and Terahertz School of Microelectronics South China University of Technology Guangzhou China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

This paper presents a V-band three-stage power amplifier (PA) implemented in 65-nm CMOS technology. A two-way parallel power combiner is used to combine two cascode amplifiers for the enhancement of output power. Neutralizing capacitors are used in the power cell and driver cell to mitigate feedback from the inherent transistor gate-drain parasitic capacitance to improve power gain and increase reverse isolation. In order to improve the output power 1-dB compression point (OP1dB), a gain response shaping method is developed using a Class-C cascode amplifier for the power cell and a Class-A common-source amplifier for the second driver cell. Simulation results show a peak small-signal gain of 18.6 dB and a 3-dB bandwidth of 11.7 GHz (52.8-64.5 GHz). This PA has a saturated output power Psat of 19.8 dBm, an OP1dB of 18.8 dBm, and a peak power-added efficiency (PAE) of 9% at 60 GHz. The compact core occupies an area of 0.201 mm 2 , and the DC power consumption is 512.7 mW.

关键词： Wireless communication Power demand Power combiners Simulation Power amplifiers Bandwidth CMOS technology Transistors Gain Power generation

来源：评论

学校读者我要写书评

暂无评论

A High Performance Region Recognition Network-Enhanced Deep CNN for Image Content Perceptual Hashing

引用

IEEE Internet of Things Journal 2025年

作者： Yang, Meihong Qi, Baolin Ma, Bin Xu, Jian Xian, Yongjin Li, Xiaolong Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Computer and Information Engineering Shandong University of Finance and Economics Jinan China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

Perceptual image hashing has emerged as a crucial forensic tool within the Internet of Things (IoT) ecosystem. Traditional perceptual hashing algorithms predominantly rely on global image features to generate hash codes, which limit their ability to represent key features of images effectively. This paper introduces a Perceptual Region Recognition Network (PRRN) to accurately identify key feature regions in images based on their texture distribution characteristics, thereby generating image perceptual hashing codes that reflect the key content of the images. At the same time, a perceptual hashing feature extraction module, which integrates a Residual Network (ResNet) and a Weighted Feature Fusion Network (WFFN), is built to extract deep semantic features of the object image. Where, ResNet is leveraged to extract high-level semantic features, while WFFN ensures the preservation of low-level local features. Furthermore, skip connections are employed to achieve content enhancements for intricate details of critical image regions. Additionally, the Mean Squared Error (MSE) loss is incorporated to enhance the accuracy of key region localization, further improving the sensitivity of image perceptual hash codes and accelerating the network’s convergence speed. Extensive experimental evaluations demonstrate that the proposed PRRN-based perceptual image hashing scheme significantly outperforms other state-of-the-art methods in terms of image feature representation capability. Specifically, it achieves an average improvement of over 1.2% in attack-resistant capability for images compared with other counterparts, making it a promising candidate for practical applications in the IoT environment. © 2014 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Enhancing Online Teaching Effectiveness Through Hand-Raising Detection 4

Enhancing Online Teaching Effectiveness Through Hand-Raising...

引用

4th International Conference on information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Dong, Zhiwei Chen, Xiaopan Shi, Yufeng Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Prediction of Internet User's Purchase Behavior Based on Mixed kernel SVM Model 5

Prediction of Internet User's Purchase Behavior Based on Mix...

引用

5th International Conference on computer Science and information Engineering, ICCSIE 2020

作者： Hu, Wen Shi, Yuxue School of Computer and Information Engineering Harbin University of Commerce Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

In order to better predict the purchase behavior of online consumers and improve the purchase conversion rate of e-commerce companies, a model of consumer purchase intention prediction based on mixed kernel function support vector machine is proposed. In the research, the prediction model of consumer purchase intention is designed as a two-classification problem, and the feature selection algorithm mRMR is used to obtain the ranking of features, seeking to obtain better or similar classification results when choosing fewer features. On its basis, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of the model. Experiments show that, to a certain extent, the constructed mixed kernel function can effectively improve the classification effect of SVM. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

Hybrid Pruning Method Based on Convolutional Neural Network Sensitivity and Statistical Threshold 2

Hybrid Pruning Method Based on Convolutional Neural Network ...

引用

2nd International Conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2021

作者： Guo, Changyi Li, Ping School of Computer and Communication Engineering Changsha University of Science and Technology Hunan Changsha410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Hunan Changsha410114 China

The hybrid pruning algorithm can not only ensure the precision of the network, but also achieve a good balance between pruning ratio and computation. However, traditional pruning algorithms use coarse-grained or fine-grained pruning networks, which have the tradeoff problem between pruning rate and computation amount. To this end, this paper presents. A hybrid pruning method of sensitivity and statistical threshold. Firstly, coarse-grained pruning is carried out on the network, and a fast sensitivity test is conducted on the convolutional layer of the network to determine the channels that need pruning within the tolerance range of network precision decline. Then, fine-grained pruning is performed on the network. Count the weights of the pruned network, calculate the thresholds of the weights of each layer, and delete the weights less than the thresholds so as to further reduce the size of the network and the amount of calculation. Hybrid pruning performs very well in AlexNet and Resnet networks. In particular, the method proposed in this paper is used in CIFAR-10 dataset, and the compression of FLOPs is 60%, while the compression of parameter number is nearly 80%. Compared with single pruning method, Hybrid pruning is better. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map Generation 39

DiffCalib: Reformulating Monocular Camera Calibration as Dif...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Xiankang Xu, Guangkai Zhang, Bo Chen, Hao Cui, Ying Guo, Dongyan College of Computer Science and Technology Zhejiang University of Technology China Zhejiang Key Laboratory of Visual Information Intelligent Processing China State Key Lab of CAD & CG Zhejiang University China

ISBN: (纸本)157735897X

Monocular camera calibration is a key precondition for numerous 3D vision applications. Despite considerable advancements, existing methods often hinge on specific assumptions and struggle to generalize across varied real-world scenarios, and the performance is limited by insufficient training data. Recently, diffusion models trained on expansive datasets have been confirmed to maintain the capability to generate diverse, high-quality images. This success suggests a strong potential of the models to effectively understand varied visual information. In this work, we leverage the comprehensive visual knowledge embedded in pre-trained diffusion models to enable more robust and accurate monocular camera intrinsic estimation. Specifically, we reformulate the problem of estimating the four degrees of freedom (4-DoF) of camera intrinsic parameters as a dense incident map generation task. The map details the angle of incidence for each pixel in the RGB image, and its format aligns well with the paradigm of diffusion models. The camera intrinsic can then be derived from the incident map with a simple non-learning RANSAC algorithm during inference. Moreover, to further enhance the performance, we jointly estimate a depth map to provide extra geometric information for the incident map estimation. Extensive experiments on multiple testing datasets demonstrate that our model achieves state-of-the-art performance, gaining up to a 40% reduction in prediction errors. Besides, the experiments also show that the precise camera intrinsic and depth maps estimated by our pipeline can greatly benefit practical applications such as 3D reconstruction from a single in-the-wild image. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization 1

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zheng, Guolin Li, Zuoyong Hu, Wenkai Fan, Haoyi Ching, Fum Yew Yu, Zhaochai Chen, Kaizhi College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlabeled data for training, however, pseudo-labels cannot correct their own errors. In this paper, we propose a semi-supervised method that uses nearest neighbor samples to obtain pseudo-labels and combines consistency regularization for image classification. Our method obtains pseudo-labels by computing the similarity of the data distribution between the weakly-augmented version of the unlabeled data and the labeled data stored in the support set and combines the consistency of the strongly-augmented version and the weakly-augmented version of the unlabeled data. We compared with several standard semi-supervised learning benchmarks and achieved a competitive performance. For example, we achieved an accuracy of 94.02 % on CIFAR-10 with 250 labels and 97.50 % on SVNH with 250 labels. It even achieved 91.59 % accuracy with only 40 labels data in the CIFAR-10. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Chinese Text Summarization Generation Based on Transformer and Temporal Convolutional Network 2

Chinese Text Summarization Generation Based on Transformer a...

引用

2nd International Conference on Electronic information technology, EIT 2023

作者： Huang, Wenming Zhou, Yaowei Xiao, Yannan Wen, Yayuan Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphics Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

The recurrent neural network model based on attention mechanism has achieved good results in the text summarization generation task, but such models have problems such as insufficient parallelism and exposure bias. In order to solve the above problems, this paper proposes a two-stage Chinese text summarization generation method based on Transformer and temporal convolutional network. The first stage uses a summary generation model that fuses Transformer and a temporal convolutional network, and generates multiple candidate summaries through beam search at the decoding end. In the second stage, contrastive learning is introduced, and the candidate summaries are sorted and scored using the Roberta model to select the final summary. Through experiments on the Chinese short text summarization dataset LCSTS, ROUGE was used as the evaluation method to verify the effectiveness of the proposed method on Chinese text summarization. © 2023 SPIE.

关键词： Convolution

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：