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arXiv 2025年

作者： Li, Yinqi Chang, Hong Hou, Ruibing Shan, Shiguang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks. Specifically, we extend the discriminative capability of pretrained frozen generative diffusion models from the classification task [1], [2] to the more complex object detection task, by "inverting" a pretrained layout-to-image diffusion model. To this end, a gradient-based discrete optimization approach for replacing the heavy prediction enumeration process, and a prior distribution model for making more accurate use of the Bayes’ rule, are proposed respectively. Empirical results show that this method is on par with basic discriminative object detection baselines on COCO dataset. In addition, our method can greatly speed up the previous diffusion-based method [1], [2] for classification without sacrificing accuracy. Code and models are available at https://***/LiYinqi/DIVE. © 2025, CC BY-NC-ND.

关键词： Object detection

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YTCNet: A Real-time Algorithm for Parcel Damage Detection with Rich Features and Attention

YTCNet: A Real-time Algorithm for Parcel Damage Detection wi...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhi Chen Cuifeng Du Yuyu Zhou Haoxuan Guan Xiujie Huang Zhefu Li Changjiang Liu Xiaotian Zhuang Xingyu Zhu Quanlong Guan Jinan University Guangzhou China Guangdong-Macao Advanced Intelligent Computing Joint Laboratory China Cetc Potevio Science & Technology Co. Ltd Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving China Guangdong Testing Institute of Product Quality Supervision China Beijing JD Zhenshi Information Technology Co. Ltd Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this study, we tackle the challenge of parcel damage detection and present YTCNet to improve both accuracy and real-time performance. We enhance the Yolov5 algorithm by integrating C3TR modules into the architecture to capture more comprehensive feature information. Furthermore, we introduce the CBAM attention module to focus on critical areas in parcel images, enhancing the model’s feature extraction capabilities. By effectively combining C3TR and CBAM modules, we successfully developed an efficient parcel damage detection algorithm. Our experimental results demonstrate the superior performance of YTCNet in accuracy and real-time processing compared to various object detection models across multiple parcel damage scenarios. Our research significantly contributes to the logistics supply chain industry by aligning with IoT development. It enables practical applications to more effectively identify parcel damage, thereby enhancing customer satisfaction.

关键词： YOLO Industries Accuracy Supply chains Transportation Feature extraction Real-time systems

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Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction

arXiv

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arXiv 2022年

作者： Cao, Tianwei Xu, Qianqian Yang, Zhiyong Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. In these work, the attention mechanism is used to select the user interested items in historical behaviors, improving the performance of the CTR predictor. Normally, these attentive modules can be jointly trained with the base predictor by using gradient descents. In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection. For such a problem, we propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper. More specifically, we use a differentiable module as our wrapping operator and then recast its learning problem as a continuous bilevel optimization. Moreover, we use a meta-learning algorithm to solve the optimization and theoretically prove its convergence. Meanwhile, we also provide theoretical analysis to show that our proposed method 1) efficiencies the wrapper-based feature selection, and 2) achieves better resistance to overfitting. Finally, extensive experiments on three public datasets manifest the superiority of our method in boosting the performance of CTR prediction. © 2022, CC0.

关键词： Recommender systems

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CB-YOLOv5 Algorithm for Small Target Detection in Aerial Images

CB-YOLOv5 Algorithm for Small Target Detection in Aerial Ima...

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Emerging technology (INCET), International Conference for

作者： Yingjie Li Yitian Wang Huaici Zhao College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

Aerial images are often plagued by background interference, and small targets with indistinct features, leading to low accuracy, high false detection rates, and high miss detection rates. To address these challenges, a small target detection algorithm based on YOLOv5, Coordinate-attention and Bidirectional-feature-pyramid-network YOLOv5 (CB-YOLOv5), is proposed in this paper. Considering the small number of pixels occupied by small targets and their indistinct features, a fourth target detection layer is added by concatenating the feature map from quadruple down-sampling during feature extraction with the feature map output from 8-fold up-sampling during feature fusion. Additionally, a coordinate attention mechanism is introduced during the feature extraction stage to improve small target localization and enhance detection accuracy. Finally, the original Path Aggregation Networks (PANet) structure is replaced with a weighted Bidirectional Feature Pyramid Network (BiFPN) structure during the feature fusion stage to improve the network’s ability to fuse feature maps of different scales. The simulation results demonstrate that the CB-YOLOv5 improves mAP 50 by 9.4%, mAP 75 by 9.7%, and mAP50:95 by 7.8% compared to the original YOLOv5s model. Thus, the effectiveness of the CB-YOLOv5 algorithm for detecting small targets in aerial images is validated.

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A Dual-Polarized Antenna For 5G N78 Application

A Dual-Polarized Antenna For 5G N78 Application

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiakang Pan Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University China College of Computer Science and Technology Harbin Engineering University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A compact dual polarization dipole antenna for the application to 5G base station is investigated, which is composed of radiator, baluns, and metal reflector, having an impendence bandwidth of 23.6% with VSWR<1.5. The dual-polarized antenna has a high ports isolation, high cross-polarization and high gain in operating bandwidth. The results indicate that this work can operate in entire N78 band and provide potential application for 5G base station.

关键词： Base stations 5G mobile communication Dipole antennas Baluns Metals Bandwidth Reflector antennas

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Crowdsourced Smartphone-Based Machine Learning for Gnss Jammer Detection and Localization

SSRN

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SSRN 2025年

作者： Han, Chao Song, Chenglong Wang, Dandan Guo, Yaping Zhen, Weimin Bai, Peirui Li, Hui Cui, Xiang Ruimin, Jin Fu, Yadi College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China School of Electronic Engineering Xidian University Xi’an710071 China China Research Institute of Radiowave Propagation Qingdao266107 China College of Science and Engineering and Shandong Key Laboratory of Ubiquitous Intelligent Computing University of Jinan Jinan250022 China

With the widespread application of the Global Navigation Satellite System (GNSS), ensuring system stability and security has become increasingly crucial. The jamming problem seriously affects signal reception. Traditional detection methods are costly and susceptible to equipment bias. In this study, leveraging the extensive coverage of mobile phones, we propose a machine learning-based approach for GNSS crowdsourcing jammer detection and localization. Firstly, employ the Random Forest algorithm (RF) to detect jamming in screened GNSS raw measurements;subsequently, utilize Gaussian process regression optimized by genetic algorithm (GA-GPR) to predict the spatial distribution of carrier-to-noise ratio (C/N0) and automatic gain control (AGC). By applying weighted fitting and gradient descent techniques on the spatial distribution of C/N0 and AGC, the location of jammers is determined based on their minimum values. To evaluate our approach, we conducted experiments in an open environment measuring 80 m × 70 m using a commercial jammer and 16 mobile phones as measurement devices. The results demonstrate that our algorithm achieves a jamming detection accuracy rate of 99.69% and accurate localization with an average error distance of 1.2456 m. © 2025, The Authors. All rights reserved.

关键词： Random forests

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Dynamics Modeling with Realistic Constraints for Robust Tracking Control of Manipulator

SSRN

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SSRN 2022年

作者： Zuo, Guoyu Li, Mi Liu, Lu Li, Jiangen Huang, Gao Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

To meet the requirements of precision assembly in industrial scenarios, an efficient, accurate and robust modeling control method has become an important research objective. This paper proposes a novel manipulator control method with three-level constraints, called the real deep Lagrangian network (Real-DeLaN). In this method, real data (also called physical data) are collected from the physical manipulator and utilized to train the Lagrangian dynamics network model to improve the migration ability from simulation to reality. The torque output of the dynamic model is corrected by real-time calculation of friction in the network. The contact force on the end effector of the manipulator is compensated based on the principle of virtual displacement work. Furthermore, a robust controller based on the dynamics model is designed, and the Lyapunov theorem is used to prove that the controlled system can achieve three different kinds of stability. The experimental results show that Real-DeLaN can better control the joints to perform trajectory tracking, reduce the friction error of the manipulator, and show better anti-interference ability. The robust controller further improves the robustness and tracking control effect of this method. Moreover, the method has higher practicability than other existing methods due to its good adaptability and migration ability. © 2022, The Authors. All rights reserved.

关键词： Manipulators

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An automatic generation method of cross-modal fuzzy creativity

An automatic generation method of cross-modal fuzzy creativi...

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作者： Zhang, Fuquan Wang, Yiou Wu, Chensheng Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Beijing Institute of Science and Technology Information Beijing China

Digital creativity is creative expression derived from cultural creativity and information technology. In order to overcome the problem in the creative generation in the condition of fuzzy and uncertain ideas, an automatic generation method of cross-modal fuzzy creativity (AGMCFC) is proposed. In this subject, fuzzy creative data sets and learning retrieval network are constructed for the sake of extracting original creative data effectively. And the logical correlations between creative objects are acquired dynamically based on the graph neural network. Creative objects and creative styles are generated by using generative adversarial nets technology and style transfer technology, respectively. Then, the projectiles, boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity. After adjusting the layout, creative works are automatically generated. A fuzzy creative generating environment is implemented. Experimental results show that the screened number of AGMCFC method is about twice as much as that of manual method, and the accuracy rate of AGMCFC method is improved compared with the manual method. AGMCFC method performs well at creative generation of fuzzy ideas automatically. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Graph neural networks

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NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing

arXiv

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arXiv 2023年

作者： Bian, Shuai Qi, Shouliang Li, Chen Yao, Yudong Teng, Yueyang College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Key Laboratory of Intelligent Computing in Medicine Ministry of Education Shenyang110169 China

Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in which the lack of prior knowledge is often the bottleneck for further performance improvement. To overcome this drawback, this paper proposes a novel CS method using non-local prior which combines the interpretability of the traditional optimization methods with the speed of network-based methods, called NL-CS Net. We unroll each phase from iteration of the augmented Lagrangian method solving non-local and sparse regularized optimization problem by a network. NL-CS Net is composed of the up-sampling module and the recovery module. In the up-sampling module, we use learnable up-sampling matrix instead of a predefined one. In the recovery module, patch-wise non-local network is employed to capture long-range feature correspondences. Important parameters involved (e.g. sampling matrix, nonlinear transforms, shrinkage thresholds, step size, etc.) are learned end-to-end, rather than hand-crafted. Furthermore, to facilitate practical implementation, orthogonal and binary constraints on the sampling matrix are simultaneously adopted. Extensive experiments on natural images and magnetic resonance imaging (MRI) demonstrate that the proposed method outperforms the state-of-the-art methods while maintaining great interpretability and speed. © 2023, CC BY.

关键词： Image reconstruction

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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