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arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed downsampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware downsampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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New Framework for Code-Mapping-based Reversible Data Hiding in JPEG Images

arXiv

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arXiv 2020年

作者： Du, Yang Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Anhui Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Communication & Electronic Engineering East China Normal University Shanghai200241 China

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

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LOOK globally, age locally: Face aging with an attention mechanism

arXiv

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arXiv 2019年

作者： Zhu, Haiping Huang, Zhizhong Shan, Hongming Zhang, Junping Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 200433 China

Face aging is of great importance for cross-age recognition and entertainment-related applications. Recently, conditional generative adversarial networks (cGANs) have achieved impressive results for face aging. Existing cGANs-based methods usually require a pixel-wise loss to keep the identity and background consistent. However, minimizing the pixel-wise loss between the input and synthesized images likely resulting in a ghosted or blurry face. To address this deficiency, this paper introduces an Attention Conditional GANs (AcGANs) approach for face aging, which utilizes attention mechanism to only alert the regions relevant to face aging. In doing so, the synthesized face can well preserve the background information and personal identity without using the pixel-wise loss, and the ghost artifacts and blurriness can be significantly reduced. Based on the benchmarked dataset Morph, both qualitative and quantitative experiment results demonstrate superior performance over existing algorithms in terms of image quality, personal identity, and age accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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Meta-Path Hierarchical Heterogeneous Graph Convolution Network for High Potential Scholar Recognition

Meta-Path Hierarchical Heterogeneous Graph Convolution Netwo...

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IEEE International Conference on Data Mining (ICDM)

作者： Yiqing Wu Ying Sun Fuzhen Zhuang Deqing Wang Xiangliang Zhang Qing He Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University China King Abdullah University of Science and Technology Saudi Arabia

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Recognizing high potential scholars has become an important problem in recent years. However, conventional scholar evaluating methods based on hand-crafted metrics can not profile the scholars in a dynamic and comprehensive way. With the development of online academic databases, large-scale academic activity data become available, which implies detailed information on the scholars' achievement and academic activities. Inspired by the recent success of deep graph neural networks (GNNs), we propose a novel solution to recognize high potential scholars on the dynamic heterogeneous academic network. Specifically, we propose a novel Mate-path Hierarchical Heterogeneous Graph Convolution Network (MHHGCN) to effectively model the heterogeneous graph information. MHHGCN hierarchically aggregates entity and relational information on a set of meta-paths, and can alleviate the information loss problem in the previous heterogenous GNN models. Then to capture the dynamic scholar feature, we combine MHHGCN with Long Short Term Memory (LSTM) network with attention mechanism to model the temporal information and predict the potential scholar. Extensive experimental results on real-world high potential scholar data demonstrate the effectiveness of our approach. Moreover, the model shows high interpretability by visualization of the attention layers.

关键词： Measurement Convolution Aggregates Data visualization Predictive models Graph neural networks Long short term memory

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A Diversity Based Competitive Multi-objective PSO for Feature Selection 1

引用

15th International Conference on intelligent Computing, ICIC 2019

作者： Qiu, Jianfeng Cheng, Fan Zhang, Lei Xu, Yi School of Computer Science and Technology Anhui University Hefei230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection has attracted increasing attention of researchers recently. However, in the existing methods, quick convergence usually degrades the diversity of the population, especially when many irrelevant and redundant features involved in them. To this end, a diversity based competitive multi-objective particle swarm optimization for feature selection problem (named D-CMOPSO) is proposed. In D-CMOPSO, a diversified competition based learning mechanism is proposed to improve the quality of found feature subset, which consists of three parts: exemplar particle construction, pairwise competition, and diversified learning strategy. The proposed competition mechanism utilizes the above three parts to boost the diversity in the following generations. Moreover, in order to guide the initial population to evolve the promising area, a maximal information coefficient based initialization strategy is also suggested. The experimental results demonstrate that the proposed D-CMOPSO is competitive for feature selection problem. © 2019, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Distributed Global Leader-Following Coordination of MASs with Heterogeneous Input Saturation: Scheduling Low-Gain Revisited

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Journal of the Franklin Institute 2025年第11期362卷

作者： Xiaoling Wang Shengnan Zhu Juan Qian Housheng Su College of Automation & AI Nanjing University of Posts and Telecommunications and Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing 210023 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China Université Claude Bernard Lyon 1 CNRS LAGEPP UMR 5007 43 boulevard du 11 novembre 1918 F-69100 Villeurbanne France National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China

In this paper, we revisit the global coordinated tracking problem of the leader-following multi-agent systems with input saturation via the scheduling low-gain feedback technique, aiming to make breakthroughs from aspects such as the heterogeneity of saturation level and the distributed characteristic of scheduled tuning algorithms on the low-gain parameters, that is the newly proposed distributed scheduling low-gain approach. First, we provide a continuous-time tuning algorithm for the low-gain parameter of each follower to articulate the fundamental idea of the distributed scheduling low-gain technique on addressing heterogeneous saturation, so as to solve the global consensus tracking of multi-agent systems with heterogeneous input saturation and one leader. Then, this result is extended to the global containment tracking of multi-agent systems with multiple leaders. Furthermore, from the perspective of reducing communication burden, we further propose a distributed scheduling low-gain parameter algorithm by using the sampled relative information between each follower and its neighbors, and offer the upper and lower bounds on the sampling period to ensure the achievement of global coordination tracking. Finally, numerical simulations are performed to verify the theoretical results.

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RCAnalyzer:visual analytics of rare categories in dynamic networks

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Frontiers of information technology & Electronic Engineering 2020年第4期21卷 491-506页

作者： Jia-cheng PAN Dong-ming HAN Fang-zhou GUO Da-wei ZHOU Nan CAO Jing-rui HE Ming-liang XU Wei CHEN State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Zhejiang Lab Hangzhou 311100China Department of Computer Science and Engineering Arizona State UniversityTempe 85281USA Intelligent Big Data Visualisation Lab Tongji UniversityShanghai 200082China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China Henan Institute of Advanced Technology Zhengzhou UniversityZhengzhou 450001China

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over *** visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network *** work focuses on detecting anomalous changing patterns in the dynamic network,the rare occurrence of which could damage the development of the entire *** this study,we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic *** proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the *** particular,a novel visualization is introduced,which represents the snapshots of a dynamic network in a series of connected triangular *** clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding *** evaluate our technique via a case study and a user *** evaluation results verify the effectiveness of our system.

关键词： Rare category detection Dynamic network Visual analytics

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Non-Local ConvLSTM for Video Compression Artifact Reduction

Non-Local ConvLSTM for Video Compression Artifact Reduction

引用

International Conference on computer Vision (ICCV)

作者： Yi Xu Longwen Gao Kai Tian Shuigeng Zhou Huyang Sun Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Bilibili Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos. Most existing approaches use a single neighboring frame or a pair of neighboring frames (preceding and/or following the target frame) for this task. Furthermore, as frames of high quality overall may contain low-quality patches, and high-quality patches may exist in frames of low quality overall, current methods focusing on nearby peak-quality frames (PQFs) may miss high-quality details in low-quality frames. To remedy these shortcomings, in this paper we propose a novel end-to-end deep neural network called non-local ConvLSTM (NL-ConvLSTM in short) that exploits multiple consecutive frames. An approximate non-local strategy is introduced in NL-ConvLSTM to capture global motion patterns and trace the spatiotemporal dependency in a video sequence. This approximate strategy makes the non-local module work in a fast and low space-cost way. Our method uses the preceding and following frames of the target frame to generate a residual, from which a higher quality frame is reconstructed. Experiments on two datasets show that NL-ConvLSTM outperforms the existing methods.

关键词： Spatiotemporal phenomena Video compression Feature extraction Image coding Task analysis Visualization Correlation

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Non-local ConvLSTM for video compression artifact reduction

arXiv

引用

arXiv 2019年

作者： Xu, Yi Gao, Longwen Tian, Kai Zhou, Shuigeng Sun, Huyang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Bilibili Shanghai China

关键词： Image compression

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Converged reconfigurable intelligent surface and mobile edge computing for space information networks

arXiv

引用

arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Zhang, Yan Niyato, Dusit Han, Zhu Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking International Joint Innovation Center Zhejiang University China Department of Informatics University of Oslo Norway School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of Houston United States

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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