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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Angchi Xu Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript, which is time-consuming and hard to be parallelized while training. In this work, we aim to escape from this inefficient alignment with massive but redundant frames, and instead to directly localize a few action transitions for pseudo segmentation generation, where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation, we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition, to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation, we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed. 1 1 Code is available at https://***/iSEE-laboratory/CVPR24_ATBA.

关键词： Training Visualization Computer vision Semantics Noise Robustness Pattern recognition

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An Efficient and Scalable Consensus for Main Blockchain in the Multi-Chain Network

An Efficient and Scalable Consensus for Main Blockchain in t...

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International Conference on Networking and Network Applications (NaNA)

作者： Xinlu Zhuang Zeyu Xiao Haoping Qin Yunjie Ge Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

While blockchain technology has gained significant popularity and adoption, auditing the legitimacy of on-chain transactions continues to present complex technical challenges. To address the challenge of auditing the legitimacy of on-chain transactions in multi-chain systems, this paper proposes an Auditing-Service Multi-Chain architecture that utilizes a new Auditing Chain to facilitate cross-chain auditing of transactions across multiple Service Chains. However, the performance of the consensus mechanism of the Auditing Chain is limited under the increasing number of Service Chains and transaction volumes. To overcome the shortcomings of the Auditing Chain's consensus mechanism, this paper introduces a new consensus that employs a sliding window approach to determine the consensus nodes' membership and automatically selects the leader node. Moreover, the communication tree structure is utilized in the broadcast phase to optimize the network overhead in the consensus stage. Additionally, this paper applies the CoSi collective signature protocol to streamline the consensus process and enhance the throughput and scalability of the Auditing Chain. The experimental results demonstrate that the proposed consensus mechanism satisfies the performance requirements of the Auditing-Service Multi-Chain architecture in a local test network environment.

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Confidence-guided Source Label Refinement and Class-aware Pseudo-Label Thresholding for Domain-Adaptive Semantic Segmentation

Confidence-guided Source Label Refinement and Class-aware Ps...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hongwei Chu Meng Shen Jiawen Peng Andy J Ma School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Domain-Adaptive Semantic Segmentation (DASS) aims to transfer a segmentation model trained on a labeled source domain to an unlabeled target domain. Most existing methods overlook the errors in the source labels and directly utilize the erroneous source labels for training. This may result in wrong source domain knowledge being wrongly transferred to the target domain, which leads to similar classes cannot be separated well in the target domain. To this end, we propose a novel Confidence-guided Online REfinement (CORE) method, which introduces a refinement network trained on the target data to rectify erroneous source labels online. This can effectively avoid transferring erroneous knowledge from the source domain to the target domain. On the other hand, using the same threshold for all classes in the target domain may result in a severe class imbalance in self-training. Therefore, we propose Class-aware Adaptive Thresholding (CAT) to calculate different thresholds for different target classes, which adaptively filter target pixels with high confidence for better adaptation. Our proposed CORE-CAT method can be easily integrated with existing methods and achieves superior performance across various domain-adaptive semantic segmentation benchmarks.

关键词： Training Adaptation models Semantic segmentation Neural networks Adaptive filters Benchmark testing

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SEMANTIC PROXIMITY ALIGNMENT: TOWARDS HUMAN PERCEPTION-CONSISTENT AUDIO TAGGING BY ALIGNING WITH LABEL TEXT DESCRIPTION

arXiv

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arXiv 2023年

作者： Liu, Wuyang Ren, Yanzhen Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China

Most audio tagging models are trained with one-hot labels as supervised information. However, one-hot labels treat all sound events equally, ignoring the semantic hierarchy and proximity relationships between sound events. In contrast, the event descriptions contains richer information, describing the distance between different sound events with semantic proximity. In this paper, we explore the impact of training audio tagging models with auxiliary text descriptions of sound events. By aligning the audio features with the text features of corresponding labels, we inject the hierarchy and proximity information of sound events into audio encoders, improving the performance while making the prediction more consistent with human perception. We refer to this approach as Semantic Proximity Alignment (SPA). We use Ontology-aware mean Average Precision (OmAP) as the main evaluation metric for the models. OmAP reweights the false positives based on Audioset ontology distance and is more consistent with human perception compared to mAP. Experimental results show that the audio tagging models trained with SPA achieve higher OmAP compared to models trained with one-hot labels solely (+1.8 OmAP). Human evaluations also demonstrate that the predictions of SPA models are more consistent with human perception. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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APICAD: Augmenting API Misuse Detection through Specifications from Code and Documents

APICAD: Augmenting API Misuse Detection through Specificatio...

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International Conference on Software engineering (ICSE)

作者： Xiaoke Wang Lei Zhao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

Using API should follow its specifications. Otherwise, it can bring security impacts while the functionality is damaged. To detect API misuse, we need to know what its specifications are. In addition to being provided manually, current tools usually mine the majority usage in the existing codebase as specifications, or capture specifications from its relevant texts in human language. However, the former depends on the quality of the codebase itself, while the latter is limited to the irregularity of the text. In this work, we observe that the information carried by code and documents can complement each other. To mitigate the demand for a high-quality codebase and reduce the pressure to capture valid information from texts, we present APICAD to detect API misuse bugs of C/C++ by combining the specifications mined from code and documents. On the one hand, we effectively build the contexts for API invocations and mine specifications from them through a frequency-based method. On the other hand, we acquire the specifications from documents by using lightweight keyword-based and NLP-assisted techniques. Finally, the combined specifications are generated for bug detection. Experiments show that APICAD can handle diverse API usage semantics to deal with different types of API misuse bugs. With the help of APICAD, we report 153 new bugs in Curl, Httpd, OpenSSL and Linux kernel, 145 of which have been confirmed and 126 have applied our patches.

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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DiffuVolume: Diffusion Model for Volume based Stereo Matching

arXiv

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arXiv 2023年

作者： Zheng, Dian Wu, Xiao-Ming Liu, Zuhao Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of MachineIntelligence and Advanced Computing Ministry of Education China

Stereo matching is a significant part in many computer vision tasks and driving-based applications. Recently cost volume-based methods have achieved great success benefiting from the rich geometry information in paired images. However, the redundancy of cost volume also interferes with the model training and limits the performance. To construct a more precise cost volume, we pioneeringly apply the diffusion model to stereo matching. Our method, termed DiffuVolume, considers the diffusion model as a cost volume filter, which will recurrently remove the redundant information from the cost volume. Two main designs make our method not trivial. Firstly, to make the diffusion model more adaptive to stereo matching, we eschew the traditional manner of directly adding noise into the image but embed the diffusion model into a task-specific module. In this way, we outperform the traditional diffusion stereo matching method by 22% EPE improvement and 240 times inference acceleration. Secondly, DiffuVolume can be easily embedded into any volume-based stereo matching network with boost performance but slight parameters rise (only 2%). By adding the DiffuVolume into well-performed methods, we outperform all the published methods on Scene Flow, KITTI2012, KITTI2015 benchmarks and zero-shot generalization setting. It is worth mentioning that the proposed model ranks 1st on KITTI 2012 leader board, 2nd on KITTI 2015 leader board since 15, July 2023. Copyright © 2023, The Authors. All rights reserved.

关键词： Information filtering

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Revisiting adversarial robustness distillation from the perspective of robust fairness 23

Revisiting adversarial robustness distillation from the pers...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xinli Yue Ningping Mou Qian Wang Lingchen Zhao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

Adversarial Robustness Distillation (ARD) aims to transfer the robustness of large teacher models to small student models, facilitating the attainment of robust performance on resource-limited devices. However, existing research on ARD primarily focuses on the overall robustness of student models, overlooking the crucial aspect of robust fairness. Specifically, these models may demonstrate strong robustness on some classes of data while exhibiting high vulnerability on other classes. Unfortunately, the "buckets effect" implies that the robustness of the deployed model depends on the classes with the lowest level of robustness. In this paper, we first investigate the inheritance of robust fairness during ARD and reveal that student models only partially inherit robust fairness from teacher models. We further validate this issue through fine-grained experiments with various model capacities and find that it may arise due to the gap in capacity between teacher and student models, as well as the existing methods treating each class equally during distillation. Based on these observations, we propose Fair Adversarial Robustness Distillation (FairARD), a novel framework for enhancing the robust fairness of student models by increasing the weights of difficult classes, and design a geometric perspective-based method to quantify the difficulty of different classes for determining the weights. Extensive experiments show that Fair-ARD surpasses both state-of-the-art ARD methods and existing robust fairness algorithms in terms of robust fairness (e.g., the worst-class robustness under AutoAttack is improved by at most 12.3% and 5.3% using ResNet18 on CIFAR10, respectively), while also slightly improving overall robustness. Our code is available at: https://***/NISP-official/Fair-ARD.

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A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition

arXiv

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arXiv 2023年

作者： Zheng, Li Li, Fei Chai, Yuyang Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. However, current methods encode the dialog context in only one direction, which limits their ability to thoroughly comprehend the context. Moreover, these methods overlook the explicit correlations between sentiment and act labels, which leads to an insufficient ability to capture rich sentiment and act clues and hinders effective and accurate reasoning. To address these issues, we propose a Bi-directional Multi-hop Inference Model (BMIM) that leverages a feature selection network and a bi-directional multi-hop inference network to iteratively extract and integrate rich sentiment and act clues in a bi-directional manner. We also employ contrastive learning and dual learning to explicitly model the correlations of sentiment and act labels. Our experiments on two widely-used datasets show that BMIM outperforms state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1 score in DSC. Additionally, Our proposed model not only improves the performance but also enhances the interpretability of the joint sentiment and act prediction task. © 2023, CC BY-NC-ND.

关键词： Iterative methods

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Two Physics-Based Schwarz Preconditioners for Three-Temperature Radiation Diffusion Equations in High Dimensions

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Communications in Computational Physics 2022年第8期32卷 829-849页

作者： Xiaoqiang Yue Jianmeng He Xiaowen Xu Shi Shu Libo Wang National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105P.R.China Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100094P.R.China College of Information Science and Technology JinanUniversityGuangzhou 510632P.R.China

We concentrate on the parallel,fully coupled and fully implicit solution of the sequence of 3-by-3 block-structured linear systems arising from the symmetrypreserving finite volume element discretization of the unsteady three-temperature radiation diffusion equations in high *** this article,motivated by[***,***,***,SIAM *** ***.33(2012)653–680]and[***,***,***,***.442(2021)110513],we aim to develop the additive and multiplicative Schwarz preconditioners subdividing the physical quantities rather than the underlying domain,and consider their sequential and parallel implementations using a simplified explicit decoupling factor approximation and algebraic multigrid subsolves to address such linear ***,computational efficiencies and parallel scalabilities of the proposed approaches are numerically tested in a number of representative real-world capsule implosion benchmarks.

关键词： Radiation diffusion equations Schwarz methods algebraic multigrid parallel and distributed computing

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