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检索条件"丛书名=Lecture Notes in Computer Science"

共 300226 条记录，以下是1221-1230 订阅

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Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering 7th

Balancing Complementarity and Consistency via Delayed Activa...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Li, Bo Xu, Zhiwei Yun, Jing Wang, Jiatai Inner Mongolia Univ Technol Coll Data Sci & Applicat Hohhot Peoples R China Haihe Lab ITAI Tianjin Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9789819787913;9789819787920

This paper study one challenging issue in incomplete multi-view clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. The delayed activation could enriches the complementarity information that was ignored during consistency learning. Then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. This could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets.

关键词： Incomplete Multi-view Clustering Contrastive Learning Complementarity Consistency Balance

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A Bispectral 3D U-Net for Rotation Robustness in Medical Segmentation 1st

A Bispectral 3D U-Net for Rotation Robustness in Medical Seg...

引用

1st International Workshop on Topology-and Graph-Informed Imaging Informatics

作者： Chevalley, Arthur Oreiller, Valentin Fageot, Julien Prior, John O. Andrearczyk, Vincent Depeursinge, Adrien Lausanne Univ Hosp CHUV Nucl Med & Mol Imaging Dept Lausanne Switzerland HES SO Valais Wallis Univ Appl Sci & Arts Western Inst Informat Sierre Switzerland

ISBN: (纸本)9783031739668;9783031739675

Segmentation models achieved expert-level performance in a large variety of medical applications. However, their robustness to rotations is rarely discussed and can be crucial for clinical use with the risk of discarding subtle but diagnostically relevant anatomical structures. In medical images, complex structures appear in a wide range of positions and rotations, requiring rotation robustness. In this work, we investigate the robustness to rotations of a standard 3D nnU-Net in the context of two segmentation tasks: the hippocampus in MRI and the pulmonary airway system in CT. In addition, we introduce a 3D Locally Rotation Invariant (LRI) operator based on the bispectrum to achieve high robustness to input rotations. It is compared to a standard nnU-Net, a nnU-Net with extended rotational data augmentation and XEdgeConv, a state-of-the-art approach for RI. While all models performed similarly in terms of Dice score for right-angle rotations, the Bispectral U-Net outperformed other designs in the context of finer and more realistic rotations. Furthermore, the Bispectral U-Net and the XEdgeConv were more stable w.r.t. input rotation, i.e. the predictions are significantly more consistent across input rotations. Important inconsistencies of the nnU-Net were observed for lung airway segmentation, suggesting potential risks of using the model in clinical routine.

关键词： Local Rotation Invariance Robust 3D Segmentation Convolutional Bispectral Network Deep Learning Medical Image Analysis

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Identifying Propagating Signals with Spatio-Temporal Clustering in Multivariate Time Series 17th

Identifying Propagating Signals with Spatio-Temporal Cluster...

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17th International Conference on Similarity Search and Applications

作者： Huewel, Jan David Schlake, Georg Stefan Albrechts, Kevin Beecks, Christian Univ Hagen Hagen Germany

ISBN: (纸本)9783031758225;9783031758232

Recordings via multiple sensors positioned on a grid result in multivariate time series, whose subsequences can be compared in aspects of time, space and shape. Existing methods for tracking propagating signals in this high-content data format require a starting position, which has to be determined by a domain expert. In this paper, we propose a fully unsupervised method to discover propagating signals via density-based clusters with respect to the three aspects mentioned above. For this purpose, we adapt the DBSCAN algorithm to our specific setting and present an exemplary application of this method on pharmacological data.

关键词： Clustering Multivariate Time Series Signal Propagation

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MOFTrack: Multi-object Formation Tracking in Remote Sensing Videos 7th

MOFTrack: Multi-object Formation Tracking in Remote Sensing ...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Yang, Lang Wang, Haoqi Sung, Haijiang Liu, Qiaoyuan Li, Tanlin Wang, Lijun Wang, Yifan Dalian Univ Technol Dalian 116024 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Beijing Peoples R China

ISBN: (纸本)9789819788576;9789819788583

Multi-object tracking typically concentrates on the tracking of individual targets without taking into account the scenario where some of these targets are part of a formation. In this paper, we define a new task called multi-object formation tracking, which requires tracking the formation as a whole while simultaneously tracking individual targets within the formation. To tackle this challenge, we propose a new Multi-Object Formation Tracking method called MOFTrack, which is composed of three main components: (1) position search, which adaptively assigns different search ranges for targets of varying sizes, (2) trajectory validation, which addresses the issue of formation instability during target intersections, and (3) effective bit motion selection, which facilitates the rapid formation tracking of newly emerging targets while also mitigating the impact of trajectory loss on the collection of motion feature vectors. To measure the accuracy of formation tracking, we introduce a novel simulated aircraft formation dataset with formation numbers for small targets in remote sensing videos. In addition, we introduce a metric called Formation Accuracy, FACC. We categorize targets into three groups to assess performance across various target pixel sizes: extremely small, relatively small and small. We successfully implement online reliable tracking and formation assigning for multiple targets in remote sensing videos, achieving high FACC for all three categories of targets. Source code and dataset will be made publicly available.

关键词： Multi-object formation tracking Remote sensing videos Simulated dataset

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Competition of Solvers for Constrained Horn Clauses (CHC-COMP 2023) 3rd

Competition of Solvers for Constrained Horn Clauses (CHC-COM...

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3rd International TOOLympics Challenge

作者： De Angelis, Emanuele Krishnan, Hari Govind Vediramana CNR IASI Rome Italy Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9783031676949;9783031676956

Constrained Horn Clauses (CHC) are a fragment of first-order logic that is expressive enough to represent many important software verification tasks, yet practical for fully automatic techniques. CHCs are used to verify the safety of hardware, software, and hybrid systems, infer refinement types, analyse program termination, and prove the correctness of smart contracts, among other applications. Over the years, many researchers have developed a variety of tools to check the satisfiability of CHCs. CHC-COMP strives to bring together these research efforts and compare the effectiveness of different CHC solvers on a unified set of benchmarks. Each year, CHC-COMP collects benchmarks provided by the CHC community, organizes them into tracks, and evaluates CHC solvers against those tracks. This annual competition is now in its sixth year. This report gives an overview of CHC-COMP and details of its sixth edition.

关键词： Constrained Horn Clauses Constraint Logic Programming Satisfiability Modulo Theories Constraint Solving

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Multi-view Spectral Clustering Based on Topological Manifold Learning 7th

Multi-view Spectral Clustering Based on Topological Manifold...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Shi, Shaojun Liu, Yibing Zhang, Canyu Chen, Xueling Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China

ISBN: (纸本)9789819784868;9789819784875

Multi-view clustering is an unsupervised learning strategy that divides data into multiple categories based on complementary and consistent information. Graph-based multi-view clustering methods have attracted much attention due to their simplicity and efficiency. Although graph-based multi-view clustering algorithms have achieved good clustering performance, there are still some issues that need to be addressed. Firstly, existing methods fail to consider the manifold topological structure in the data, which might cause that the constructed similarity graphs are low-quality. Secondly, many graph-based multi-view clustering algorithms treat the construction of similarity graphs and the learning of consistent spectral embedding as two separate procedures, in which the quality of similarity graphs heavily affects the clustering performance. To overcome these problems, we propose a novel method termed as Multi-view Spectral Clustering based on Topological Manifold Learning (MSCTML), where both similarity graph construction and consistent spectral embedding learning are jointly performed in an unified framework. Concretely, affine graph is initially constructed for each view. Subsequently, considering the manifold topological structure in the data, similarity graphs for different views are generated by using the above affine graphs. Furthermore, consistent spectral embedding is learned based on the constructed similarity graphs. Finally, the clustering result is obtained by K-means algorithm. The proposed method is tested on six benchmark datasets. Comparing with single-view and state-of-the-art multi-view clustering algorithms, extensive experimental results demonstrate the superior clustering performance of the proposed MSCTML method.

关键词： Multi-view clustering Manifold learning Spectral embedding

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AI-Boosted Decision Techniques for Strategy Formulation and Implementation 2nd

AI-Boosted Decision Techniques for Strategy Formulation and ...

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2nd Decision science Alliance International Summer Conference-DSA ISC

作者： Gamboa, Lander Globant Barcelona Spain

ISBN: (纸本)9783031782374;9783031782381

Decision science, by combining techniques like Generative Intelligence and Retrieval-Augmented Generation, System Dynamics, and Strategy Mapping with the Balanced Scorecard, can become a powerful tool for businesses to not only make informed decisions but also gain a deeper understanding of their resources as identified by the Resource-Based View of the Firm. The understanding can become quantitative by creating either a higher-level System Dynamics model or a detailed Digital Twin. Either way, this integrated approach fosters a more comprehensive picture of a company's assets and capabilities and enables a more sophisticated and effective formulation of the company strategy. Finally, the principles and techniques of the Balanced Scorecard can be adapted based on the capabilities of modern AI systems to maintain a tighter control on strategy implementation.

关键词： Generative AI Retrieval Augmented Generation Resource Based View System Dynamics Balanced Scorecard

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Accelerating Domain Adaptation with Cascaded Adaptive Vision Transformer 7th

Accelerating Domain Adaptation with Cascaded Adaptive Vision...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Jiang, Qilin Cui, Chaoran Zhang, Chunyun Zhen, Yongrui Gong, Shuai Liu, Ziyi Meng, Fan'an Zhao, Hongyan Shandong Univ Finance & Econ Jinan Peoples R China

ISBN: (纸本)9789819784868;9789819784875

Domain adaptation (DA) aims to transfer knowledge from labeled source domains to unlabeled target domains, addressing the challenge of model generalization when there is a distribution mismatch between training and testing data. While many Vision Transformer (ViT)-based methods have been developed for DA, they focus primarily on improving accuracy, with less emphasis on accelerating inference on unlabeled target domains. In this paper, we propose a novel method named Cascaded Adaptive Vision Transformer (CAViT), which dynamically adjusts token counts for each input image by cascading multiple transformers with increasing tokens. During testing, "easier" images exit early, while "harder" images are processed further until confident predictions are achieved. We further enhance domain adversarial learning by incorporating a token-level domain discriminator in the attention layer, which assigns distinct weights to different patch tokens. This enables the network to learn features with cross-domain transferability and discriminative capabilities, achieving effective feature alignment. Experimental results demonstrate that our method not only improves accuracy but also significantly reduces computational costs, as evidenced by results on three benchmark datasets.

关键词： Domain adaptation Transfer learning Adaptive inference Resource efficiency

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AI Act High-Risk Requirements Readiness: Industrial Perspectives and Case Company Insights 25th

AI Act High-Risk Requirements Readiness: Industrial Perspect...

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25th International Conference on Product Focused Software Process Improvement

作者： Wagner, Matthias Gupta, Rushali Borg, Markus Engstrom, Emelie Lysek, Michal Lund Univ Dept Comp Sci POB 118 S-22100 Lund Sweden

ISBN: (纸本)9783031783913;9783031783920

The AI Act's (AIA) requirements for high-risk AI systems affect many aspects of modern software systems. Knowing which AI-Arelated technical challenges are relevant to different companies is essential to focus compliance-oriented research on the aspects that matter. We therefore conducted an interview study in collaboration with a case company that specializes in network video solutions within the security and surveillance industry. External experts enrich the study for a broader industry perspective. The goal was to analyze the case company's readiness for the AIA's high-risk requirements, based on methods and techniques already established prior to the legislation. Our results yielded a positive sentiment towards the regulation and the planning security that it brings, although a high workload was expected. We identified a solid foundation with well-established practices to build upon for the requirements on cybersecurity, human oversight, record-keeping, and technical documentation. However, we also report several open challenges, mainly connected to the requirement on data quality and governance, followed by accuracy, robustness, and cybersecurity. The AIA specifically demands a post-market monitoring system (Art 72) and the right to an explanation of individual decision-making (Art 86). These two obligations were identified as especially challenging by the respondents. The result of this study is expected to steer future compliance-oriented work toward pressing challenges.

关键词： AI Act Interview Study Regulatory Requirements

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DARTS-CGW: Research on Differentiable Neural Architecture Search Algorithm Based on Coarse Gradient Weighting 7th

DARTS-CGW: Research on Differentiable Neural Architecture Se...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Liu, Wenbo Deng, Tao An, Rui Yan, Fei Southwest Jiaotong Univ Chengdu Peoples R China 20th Res China Elect Technol Grp Corp Xian Peoples R China

ISBN: (纸本)9789819785018;9789819785025

Differential architecture search (DARTS) has emerged as a prominent research area, yet it grapples with a longstanding challenge: the discretization discrepancy problem. This issue directly impedes the search for an optimal model architecture and undermines search algorithm performance. To alleviate this issue, we propose a novel coarse gradient weighting algorithm. Our proposed algorithm has the capability to simulate the discretization process, wherein the architectural parameters move toward both ends. And we integrate this discretization process into the training phase of the architectural parameters, enabling the model to adapt to the discretization process in a trial-and-error fashion. Specifically, based on the architectural parameters in training, we divide the candidate operations into two regions, i.e., the easy-to-select region and the hard-to-be-selected region. The different weighting strategies are implemented in different regions so that the architectural parameters are pushed to the ends. The processed architecture parameters are used for training, which is equivalent to introducing the discretization process into the search phase. Additionally, we use the coarse gradient algorithm to optimize the updating process of the weighting algorithm and theoretically justify the rationality of the coarse gradient weighting algorithm. Extensive experimental results demonstrate that our proposed method can improve the performance of the searched model and make DARTS more robust without adding additional search time.

关键词： Coarse gradient Differentiable neural architecture search Automated machine learning

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