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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Duo Su Junjie Hou Weizhi Gao Yingjie Tian Bowen Tang UCAS School of Computer Science and Technology Research Center on Fictitious Economy and Data Science CAS Key Laboratory of Big Data Mining and Knowledge Management CAS UCAS Sino-Danish College Department of Computer Science NCSU UCAS School of Economics and Management UCAS MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation CAS Institute of Computing Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce dataset Distillation via Disentangled Diffusion Model (D 4 M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D 4 M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects.

关键词： Training Computer vision Computational modeling Prototypes Computer architecture Diffusion models Pattern recognition

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Deepfake network Architecture Attribution

arXiv

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arXiv 2022年

作者： Yang, Tianyun Huang, Ziyao Cao, Juan Li, Lei Li, Xirong Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial network (GAN) models and obtain high accuracies. While encouraging, these works are restricted to model-level attribution, only capable of handling images generated by seen models with a specific seed, loss and dataset, which is limited in real-world scenarios when fake images may be generated by privately trained models. This motivates us to ask whether it is possible to attribute fake images to the source models’ architectures even if they are finetuned or retrained under different configurations. In this work, we present the first study on Deepfake network Architecture Attribution to attribute fake images on architecture-level. Based on an observation that GAN architecture is likely to leave globally consistent fingerprints while traces left by model weights vary in different regions, we provide a simple yet effective solution named DNA-Det for this problem. Extensive experiments on multiple cross-test setups and a large-scale dataset demonstrate the effectiveness of DNA-Det. Our source code and dataset can be found here: https://***/ICTMCG/DNA-Det Copyright © 2022, The Authors. All rights reserved.

关键词： network architecture

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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater networks and Systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

arXiv

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arXiv 2024年

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm’s generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024, CC BY.

关键词： Semantic Segmentation

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UAV-Assisted Covert data Collection: When Remote Monitoring Meets Covert Communication

UAV-Assisted Covert Data Collection: When Remote Monitoring ...

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International Conference on Computer and Communications (ICCC)

作者： Huasheng Su Xiaodong Xu Jing Zhao Jiaxi Zhou Key Lab. of WOC-CAS University of Science and Technology of China Hefei Anhui China Institute of Space Integrated Ground Network Hefei Anhui China

Unmanned aerial vehicle (UAV) assisted remote monitoring and data collection are prevailing applications thanks to its deployment flexibility and strong mobility. To meet security and privacy requirements, this work investigates a UAV-assisted covert data collection scenario, where the collection scheduling, three-dimensional trajectory and transmit power of the involved UAV are jointly optimized while communicating with another UAV receiver in the presence of adversaries. Taking into account the channel uncertainty at the transmitter, the optimization problem is first formulated to maximize the average covert communication rate for the worst case and the average case, respectively. Then, due to the mixed-integer non-convex nature of the optimization problem, the penalty successive convex approximation (P-SCA) scheme is introduced as a solver in association with proper mathematical approximation of some constraints. The numerical results are finally provided to validate the covertness performance of the proposed scheme, which outperforms the baseline who has no information of channel mismatch.

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Rank Internet Service Quality Using Multiple Features: A Machine Learning Approach

Rank Internet Service Quality Using Multiple Features: A Mac...

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Dandan Tu Shuang Wang Haiyan Yu Tao Zhu Chengchun Shu Jingwei Shi Key Laboratory of Network Science and Technology ICT Institute of Computing Technology CAS P.O.Bo NEC Labs China 11F Bldg Innovation Plaza Tsinghua Science Park Bei jing 100084 China

This paper addresses the problem of Ranking Internet service quality by taking a machine learning approach using multiple service features. Ranking helps find good services for applications that use services as building blocks. Unlike other ranking problems, the goodness of Internet service qualities is dependent upon multiple key features. The key features vary across different service categories and have unequally discriminative natures. This paper divides the ranking problem into four subtasks including categorizing services according to the service functionalities, identifying key features that determine the quality, denoising for feature measurement values and computing global ranking scores with multiple key features, which are cast into machine learning problems and solved using techniques classification, feature selection, clustering, and regression respectively. In particular, we propose in this paper an efficient dense-block based denoising method for subjective features, and a Supported Vector Regression based method for computing global ranking scores. Experimental results on both synthetic and real data show that the proposed approach can quantitatively identify the key features across service categories, discard noisy measurement values in 10 times faster, and compute the global ranking scores using multiple features with low mean squared errors for both linear and nonlinear ranking functions.

关键词： Web and internet services Machine learning Computational modeling Training data Support vector machines Clustering algorithms Web services

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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Iterative network pruning with uncertainty regularization for lifelong sentiment classification

arXiv

引用

arXiv 2021年

作者： Geng, Binzong Yang, Min Yuan, Fajie Wang, Shupeng Ao, Xiang Xu, Ruifeng University of Science and Technology of China China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Westlake University Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology CAS Harbin Institute of Technology Shenzhen China Tencent

Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as continually training of incrementally available information inevitably results in catastrophic forgetting or interference. In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization. By performing network pruning with uncertainty regularization in an iterative manner, IPRLS can adapt a single BERT model to work with continuously arriving data from multiple domains while avoiding catastrophic forgetting and interference. Specifically, we leverage an iterative pruning method to remove redundant parameters in large deep networks so that the freed-up space can then be employed to learn new tasks, tackling the catastrophic forgetting problem. Instead of keeping the old-tasks fixed when learning new tasks, we also use an uncertainty regularization based on the Bayesian online learning framework to constrain the update of old tasks weights in BERT, which enables positive backward transfer, i.e. learning new tasks improves performance on past tasks while protecting old knowledge from being lost. In addition, we propose a task-specific low-dimensional residual function in parallel to each layer of BERT, which makes IPRLS less prone to losing the knowledge saved in the base BERT network when learning a new task. Extensive experiments on 16 popular review corpora demonstrate that the proposed IPRLS method significantly outperforms the strong baselines for lifelong sentiment classification. For reproducibility, we submit the code and data at: https://***/siat-nlp/IPRLS. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Online low-rank similarity function learning with adaptive relative margin for cross-modal retrieval

Online low-rank similarity function learning with adaptive r...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiling Wu Shuhui Wang Weigang Zhang Qingming Huang School of Computer and Control Engineering University of Chinese Academy of Sciences China Key Lab of Intell. Info. Process Institute of Computing Technology CAS China School of Computer Science and Technology Harbin Institute of Technology at Weihai China

ISBN: (纸本)9781509060689

This paper presents a Cross-Modal Online Low-Rank Similarity function learning method (CMOLRS) for cross-modal retrieval, which learns a low-rank bilinear similarity measure on data from different modalities. CMOLRS models the cross-modal relations by relative similarities on a set of training data triplets and formulates the relative relations as convex hinge loss functions. By adapting the margin of hinge loss using information from feature space and label space for each triplet, CMOLRS effectively captures the multi-level semantic correlation among cross-modal data. The similarity function is learned by online learning in the manifold of low-rank matrices, thus good scalability is gained when processing large scale datasets. Extensive experiments are conducted on three public datasets. Comparisons with the state-of-the-art methods show the effectiveness and efficiency of our approach.

关键词： Conferences Multimedia communication

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Mesh Variational Autoencoders with Edge Contraction Pooling

Mesh Variational Autoencoders with Edge Contraction Pooling

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yu-Jie Yuan Yu-Kun Lai Jie Yang Qi Duan Hongbo Fu Lin Gao Institute of Computing Technology CAS Beijing Key Laboratory of Mobile Computing and Pervasive Device School of Computer Science and Informatics Cardiff University UK SenseTime Research City University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based convolutions, the lack of an effective pooling operation restricts the learning capability of their networks. In this paper, we propose a novel pooling operation for mesh datasets with the same connectivity but different geometry, by building a mesh hierarchy using mesh simplification. For this purpose, we develop a modified mesh simplification method to avoid generating highly irregularly sized triangles. Our pooling operation effectively encodes the correspondence between coarser and finer meshes in the hierarchy. We then present a variational auto-encoder (VAE) structure with the edge contraction pooling and graph-based convolutions, to explore probability latent spaces of 3D surfaces and perform 3D shape generation. Our network requires far fewer parameters than the original mesh VAE and thus can handle denser models thanks to our new pooling operation and convolutional kernels. Our evaluation also shows that our method has better generalization ability and is more reliable in various applications, including shape generation and shape interpolation.

关键词： Three-dimensional displays Shape Strain Interpolation Neural networks Machine learning

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