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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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XFeat: Accelerated Features for Lightweight Image Matching

XFeat: Accelerated Features for Lightweight Image Matching

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ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Potje, Guilherme Cadar, Felipe Araujo, Andre Martins, Renato Nascimento, Erickson R. Univ Fed Minas Gerais Belo Horizonte MG Brazil Univ Bourgogne CNRS UMR 6303 ICB Dijon France Google Res Mountain View CA USA Univ Lorraine LORIA INRIA Lorraine France Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9798350353013;9798350353006

We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamental design choices in convolutional neural networks for detecting, extracting, and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to resource-limited devices. In particular, accurate image matching requires sufficiently large image resolutions - for this reason, we keep the resolution as large as possible while limiting the number of channels in the network. Besides, our model is designed to offer the choice of matching at the sparse or semi-dense levels, each of which may be more suitable for different downstream applications, such as visual navigation and augmented reality. Our model is the first to offer semi-dense matching efficiently, leveraging a novel match refinement module that relies on coarse local descriptors. XFeat is versatile and hardware-independent, surpassing current deep learning-based local features in speed (up to 5x faster) with comparable or better accuracy, proven in pose estimation and visual localization. We showcase it running in real-time on an inexpensive lap-top CPU without specialized hardware optimizations. Code and weights are available at ***/descriptors/xfeat_cvpr24.

关键词： Fast Image matching Lightweight local features

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Fault-adaptive Scheduling for Data Acquisition networks 48

Fault-adaptive Scheduling for Data Acquisition Networks

引用

48th conference on local computer networks

作者： Stein, Eloise Bramas, Quentin Colombo, Tommaso Pelsser, Cristel Univ Strasbourg Strasbourg France CERN Prevessin Moens France UCLouvain Ottignies Belgium

ISBN: (纸本)9798350300741;9798350300734

Supporting such an all-to-all traffic matrix is challenging as it can easily lead to congestion. Scheduling patterns are designed to avoid such congestion by spreading the communications over time. The time is divided in phases and communications are spread across the phases. However, current scheduling algorithms are not fault-tolerant. In this paper we propose a fault-adaptive congestion-free scheduling to support an all-to-all exchange in fat tree topology. Our approach consist in the computation of the minimum number of communication phases required to support the all-to-all exchange with the available links, and of the scheduling of the communications on these phases. It enables to recover from failures and makes optimal use of the remaining bandwidth. We show that our scheduling approach provides better performance than the most common approach which is the Linear-shift scheduling. The throughput is improved by roughly 80% with our approach, for as little as one link failure.

关键词： all-to-all fat-tree networks integer linear programming

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Multi-Order Clustering on Dynamic networks: On Error Accumulation and Its Elimination

Multi-Order Clustering on Dynamic Networks: On Error Accumul...

引用

ieee conference on computer Communications (ieee INFOCOM)

作者： Gao, Yang Zhang, Hongli Harbin Inst Technol Sch Cyberspace Sci Harbin Peoples R China

ISBN: (纸本)9798350383515;9798350383508

local clustering aims to find a high-quality cluster near a given vertex. Recently, higher-order units are introduced to local clustering, and the underlying information has been verified to be essential. However, original edges are underestimated in these techniques, leading to the degeneration of network information. Moreover, most of the higher-order models are designed for static networks, whereas real-world networks are generally large and evolve rapidly. Repeatedly conducting a static algorithm at each snapshot is usually computationally impractical, and recent approaches instead track a cluster by updating the cluster sequentially. However, errors would accumulate over lengthy evolutions, and the complete cluster needs to be recalculated periodically to maintain the accuracy, which naturally affects the efficiency. To bridge the two gaps, we design a multi-order hypergraph, and present a hybrid model for dynamic clustering. In particular, we propose an incremental method to track a personalized PageRank vector in the evolving hypergraph, which converges to the exact solution at each snapshot when significantly reducing the complexity. We further develop a dynamic sweep to identify a cut in each vector, whereby a cluster can be incrementally updated with no accumulated errors. We provide rigorous theoretical basis and conduct comprehensive experiments, which demonstrate the effectiveness.

关键词： cluster tracking information degeneration error accumulation incremental personalized PageRank

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Enhancing Facial Expression Recognition by Integrating Global Dependencies with Modified Non-local Convolutional Neural networks

Enhancing Facial Expression Recognition by Integrating Globa...

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2024 ieee International conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Aquib, Mohd Verma, Nishchal K. Akhtar, M. Jaleel Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur India

ISBN: (纸本)9798350376876

Facial Expression Recognition (FER) has advanced significantly with the use of Convolutional Neural networks (CNNs), a prominent vision backbone. Despite these advancements, CNNs face limitations in capturing global dependencies across multiple facial units due to their reliance on spatial locality, leading to suboptimal performance in FER tasks. To address this issue, we propose integrating a modified non-local block into CNN-based architectures. This block effectively models non-local pixel interactions while ensuring stable dynamics, allowing for the exploration of complex non-local patterns. Our approach preserves global information and enhances CNNs' ability to identify structural relationships between different facial units, thereby improving FER performance. We validate the effectiveness of this method through experimental evaluations on three public FER datasets - CK+, JAFFE, and KDEF - where it outperforms baseline methods, demonstrating its superiority in FER tasks. The practical implications of this work suggest that enhancing CNNs with non-local modeling capabilities can lead to more accurate and robust FER systems, beneficial for applications in human-computer interaction and emotional AI systems. © 2024 ieee.

关键词： Convolutional neural networks

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local Attention: Enhancing the Transformer Architecture for Efficient Time Series Forecasting

Local Attention: Enhancing the Transformer Architecture for ...

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International Joint conference on Neural networks (IJCNN)

作者： Aguilera-Martos, Ignacio Herrera-Poyatos, Andres Luengo, Julian Herrera, Francisco Univ Granada Andalusian Inst Data Sci & Computat Intelligence Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9798350359329;9798350359312

Transformers have emerged as a highly effective architecture for natural language processing and computer vision. Of late, there has been a surge in initiatives aimed at refining this architecture to enhance its applicability to long sequence time-series forecasting, yielding promising outcomes. This paper introduces local Attention, an efficient attention mechanism tailored for time series data. This mechanism exploits the continuity properties of time series and the principle of locality in order to compute less attention scores. We provide an T(n log n) algorithm to implement local Attention based on tensor algebra results, which contrasts to the theta(n(2)) time and memory complexity of the original attention mechanism. Our experimental analysis shows that the vanilla transformer with local Attention outperforms state of the art models based on probabilistic attention mechanisms. These findings affirm the effectiveness of our approach and outline a spectrum of future challenges in long sequence time series forecasting.

关键词： time series transformer attention mechanism deep learning

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Light-Weight Monocular Depth Estimation by Non-local Decoder-Squeeze-and-Excitation Network 11

Light-Weight Monocular Depth Estimation by Non-Local Decoder...

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11th ieee International conference on Consumer Electronics - Taiwan (ICCE-Taiwan) - Empower of Innovative Consumer Technology

作者： Lin, Yz-Heng Wan, Wei-Chung Su, Hsiu-Wei Tsai, Tsung-Han Natl Cent Univ Dept Elect Engn Taoyuan Taiwan

ISBN: (纸本)9798350386851;9798350386844

In computer vision, Monocular depth estimation is an important topic. Recently the CNNs (Convolutional Neural networks) based model shows a reasonable result from an end-to-end encoder-decoder architecture. In our prior experiment, Non-local Decoder-Squeeze-and-Excitation (NL-DSE) [1] was proposed. NL-DSE is based on an Efficient-Net-B5 encoder network, but the algorithmic complexity is still high. In this paper, we aim to achieve lightweight depth estimation. To accomplish this, we replace Efficient-Net-B5 with different encoder networks and compare the performance of the modules. We evaluate the accuracy of each module on the NYU Depth V2 dataset and use Nvidia AGX Xavier as our edge device to get FLOP and frame rate. Finally, we select Efficient-Net-B0 as the encoder network to achieve the lightweight monocular depth estimation.

关键词： CNNs Monocular Depth Estimation Non-local Decoder-Squeeze-and-Excitation Light-weight depth estimation

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SYPER: Synthesis of Perfectly Resilient local Fast Re-Routing Rules for Highly Dependable networks

SYPER: Synthesis of Perfectly Resilient Local Fast Re-Routin...

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ieee conference on computer Communications (ieee INFOCOM)

作者： Gyoergyi, Csaba Larsen, Kim G. Schmid, Stefan Srba, Jiri Univ Vienna Vienna Austria Aalborg Univ Aalborg Denmark TU Berlin Berlin Germany

ISBN: (纸本)9798350383515;9798350383508

Modern communication networks support local fast re-routing (FRR) to quickly react to link failures. However, configuring such FRR mechanisms is challenging as the rules have to be defined ahead of time, without knowledge of the failures, and can depend only on local decisions made by the nodes incident to a failed link. Designing failover protection against multiple link failures is particularly difficult. We present a novel synthesis approach which addresses this challenge by generating FRR rules in an automated and provably correct manner. Our network model assumes that each node maintains a prioritised list of backup links (a.k.a. skipping forwarding)an FRR method that allows for a memory-efficient deployment. We study the theoretical properties of the model and implement a synthesis method in our tool SYPER that aims to provide perfect resilience: if there are up to k link failures, we can always route traffic between any two nodes as long as they are still connected in the underlying physical network. To this end, SYPER focuses on the synthesis of efficient forwarding rules using the BDD (binary decision diagram) methodology and our empirical evaluation shows that SYPER is feasible, and can synthesize robust network configuration in realistic settings.

关键词： Fast Re-Routing Data Plane Resilience Formal Methods Binary Decision Diagrams

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Analyzing the Characteristics of Ego-Network Structures of Influential Twitter Users 48

Analyzing the Characteristics of Ego-Network Structures of I...

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48th Annual ieee International computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Nakamitsu, Yuto Tsugawa, Sho Tsukamoto, Keiichiro Igari, Shintaro Univ Tsukuba Grad Sch Sci & Technol Tsukuba Ibaraki Japan Univ Tsukuba Inst Syst & Informat Engn Tsukuba Ibaraki Japan KADOKAWA Connected Inc Chiyoda Ku Tokyo Japan

ISBN: (纸本)9798350376975;9798350376968

We investigate the structural characteristics of ego networks of influential Twitter users. While existing studies have elucidated the global characteristics of influencers in a social network, it remains unclear what differences exist in the local structure of social networks between influencers and non-influencers. Using four Twitter datasets, we calculate two types of influence scores for each user, called direct influence and indirect influence. Then, we compare several measures of ego networks between users with high influence scores (influencers) and everyone else (non-influencers). Our results show that influencers with strong direct influence have relatively lower values for their average degree, clustering coefficient, and reciprocity, and higher centralization than non-influencers, which suggests that influencers have different ego-network structures than non-influencers. In contrast, our results also show that ego-network structures of users with strong indirect influence are not very different from those of other users, which suggests that the ego-network structure of a user is not useful for estimating their indirect influence.

关键词： Ego network Influencer Social media Social networks

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Leveraging local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks 23

Leveraging Local Patch Differences in Multi-Object Scenes fo...

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23rd ieee/CVF Winter conference on Applications of computer Vision (WACV)

作者： Aich, Abhishek Li, Shasha Song, Chengyu Asif, M. Salman Krishnamurthy, Srikanth V. Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781665493468

State-of-the-art generative model-based attacks against image classifiers overwhelmingly focus on single-object (i.e., single dominant object) images. Different from such settings, we tackle a more practical problem of generating adversarial perturbations using multi-object (i.e., multiple dominant objects) images as they are representative of most real-world scenes. Our goal is to design an attack strategy that can learn from such natural scenes by leveraging the local patch differences that occur inherently in such images (e.g. difference between the local patch on the object 'person' and the object 'bike' in a traffic scene). Our key idea is to misclassify an adversarial multi-object image by confusing the victim classifier for each local patch in the image. Based on this, we propose a novel generative attack (called local Patch Difference or LPD-Attack) where a novel contrastive loss function uses the aforesaid local differences in feature space of multi-object scenes to optimize the perturbation generator. Through various experiments across diverse victim convolutional neural networks, we show that our approach outperforms baseline generative attacks with highly transferable perturbations when evaluated under different white-box and black-box settings.

关键词： computer vision Perturbation methods Computational modeling Closed box Generators Convolutional neural networks Glass box

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Sustainable Cooperation in Peer-To-Peer networks 48

Sustainable Cooperation in Peer-To-Peer Networks

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48th conference on local computer networks

作者： Nasrulin, Bulat Chotkan, Rowdy Pouwelse, Johan Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798350300741;9798350300734

Traditionally, peer-to-peer systems have relied on altruism and reciprocity. Although incentive-based models have gained prominence in new-generation peer-to-peer systems, it is essential to recognize the continued importance of cooperative principles in achieving performance, fairness, and correctness. The lack of this acknowledgment has paved the way for selfish peers to gain unfair advantages in these systems. As such, we address the challenge of selfish peers by devising a mechanism to reward sustained cooperation. Instead of relying on global accountability mechanisms, we propose a protocol that naturally aggregates local evaluations of cooperation. Traditional mechanisms are often vulnerable to Sybil and misreporting attacks. However, our approach overcomes these issues by limiting the benefits selfish peers can gain without incurring any cost. The viability of our algorithm is proven with a deployment to 27,259 Internet users and a realistic simulation of a blockchain gossip protocol. We show that our protocol sustains cooperation even in the presence of a majority of selfish peers while incurring only negligible overhead.

关键词： peer-to-peer cooperation reputation accountability networks

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