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第33届中国控制与决策会议

作者： Wan Dai Hongpeng Wang Yulin Song Yunwei Xin College of Computer Science Nankai University Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Nankai University Research Laboratory of Machine Learning and Pervasive Computing Nankai University

ISBN: (纸本)9781665431293

Visual tele-observation is an effective way for intelligent monitoring and protection of ecology and the natural *** from pedestrian or rigid body detection,wildlife detection in natural scenes face more complex problems,such as the existence of wild background clutter,local or global vegetation occlusion of animals,small object,rotation,deformation,and other interfering *** this paper,We mainly propose a novel method for the small object *** our small object wild species data set,we use an SSD detector for object ***,the K-means algorithm is used to adjust the anchor box of the SSD ***,for the situation that SSD is not good for small object detection,in conv4 layers of SSD,the feature enhancement module is ***,aiming at the size of a small object,the network level of SSD is *** have proved the feasibility of each of the three methods through experiments and combined with the three methods to verify,the recognition rate of the small target has increased by 2.67%.

关键词： anchor feature enhancement hierarchical design wildlife detection

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ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, Word Segmentation and Entity Dictionary 2nd

ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, ...

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2nd EAI International Conference on Applied Cryptography in computer and Communications, AC3 2022

作者： Lv, Haifeng Ding, Yong School of Data Science and Software Engineering Wuzhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9783031170805

Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, one hand, since the lattice structure is dynamic and complex, although some existing lattice-based models are effectively utilize the parallel computation of GPUs, they do not fully utilize word segmentation boundary tags that as features are helpful for NER task. On the other hand, the character-word vector needs to be trained, and the user-defined entity dictionary cannot be effectively used. In this paper, we propose ALFLAT: based on a flat-lattice Transformer to incorporate ALBERT pre-trained model, word segmentation information and user-defined entity dictionary for Chinese NER. ALFLAT converts the lattice structure into a flat structure consisting of spans, integrate word segmentation embedding with the output of flat-lattice Transformer model, then modifies the emission scores according to the user-defined entity dictionary, finally utilize Viterbi decoding of the CRF layer to obtain the correct entity results. Each span corresponds to a character or latent word and its position in the original lattice. With the power of ALBERT pre-trained model, Transformer and position encoding, ALFLAT can fully leverage the lattice, word segmentation and user-defined entity dictionary information. Experiments on MSRA dataset show ALFLAT outperforms other lexicon-based models in performance and efficiency. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computational linguistics

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Efficient Multi-Query Oriented Continuous Subgraph Matching 40

Efficient Multi-Query Oriented Continuous Subgraph Matching

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ma, Ziyi Yang, Jianye Zhou, Xu Xiao, Guoqing Wang, Jianhua Yang, Liang Li, Kenli Lin, Xuemin School of Artificial Intelligence Hebei University of Technology China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control China Cyberspace Institute of Advanced Technology Guangzhou University China PengCheng Laboratory Department of New Networks China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (纸本)9798350317152

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings. © 2024 IEEE.

关键词： Directed graphs

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Light field imaging for computer vision:a survey

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Frontiers of Information Technology & Electronic Engineering 2022年第7期23卷 1077-1097页

作者： Chen JIA Fan SHI Meng ZHAO Shengyong CHEN Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China

Light field(LF)imaging has attracted attention because of its ability to solve computer vision *** this paper we briefly review the research progress in computer vision in recent *** most factors that affect computer vision development,the richness and accuracy of visual information acquisition are *** imaging technology has made great contributions to computer vision because it uses cameras or microlens arrays to record the position and direction information of light rays,acquiring complete three-dimensional(3D)scene *** imaging technology improves the accuracy of depth estimation,image segmentation,blending,fusion,and 3D *** has also been innovatively applied to iris and face recognition,identification of materials and fake pedestrians,acquisition of epipolar plane images,shape recovery,and LF ***,we further summarize the existing problems and the development trends of LF imaging in computer vision,including the establishment and evaluation of the LF dataset,applications under high dynamic range(HDR)conditions,LF image enhancement,virtual reality,3D display,and 3D movies,military optical camouflage technology,image recognition at micro-scale,image processing method based on HDR,and the optimal relationship between spatial resolution and four-dimensional(4D)LF information *** imaging has achieved great success in various *** the past 25 years,more than 180 publications have reported the capability of LF imaging in solving computer vision *** summarize these reports to make it easier for researchers to search the detailed methods for specific solutions.

关键词： Light field imaging Camera array Microlens array Epipolar plane image computer vision

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Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Wu, Xiaoxiao Gao, Zhenguo Chen, Xiaowei Wang, Yakai Qu, Shulei Li, Na Department of Computer Science and Technology Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Department of Mechanical Engineering and Automation Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Key Laboratory of Computer Vision and Machine Learning Fujian Provincial Universities Fujian Xiamen361021 China

In recent years, deep learning based on Convolutional Neural Networks (CNNs) has achieved remarkable success in many applications. However, their heavy reliance on extensive labeled data and limited generalization ability to unseen classes pose challenges to their suitability for medical image processing tasks. Few-shot learning, which utilizes a small amount of labeled data to generalize to unseen classes, has emerged as a critical research area, attracting substantial attention. Currently, most studies employ a prototype-based approach, in which prototypical networks are used to construct prototypes from the support set, guiding the processing of the query set to obtain the final results. While effective, this approach heavily relies on the support set while neglecting the query set, resulting in notable disparities within the model classes. To mitigate this drawback, we propose a novel Support-Query Prototype Fusion Network (SQPFNet). SQPFNet initially generates several support prototypes for the foreground areas of the support images, thus producing a coarse segmentation mask. Subsequently, a query prototype is constructed based on the coarse segmentation mask, additionally exploiting pattern information in the query set. Thus, SQPFNet constructs highquality support-query fused prototypes, upon which the query image is segmented to obtain the final refined query mask. Evaluation results on two public datasets, SABS and CMR, show that SQPFNet achieves state-of-the-art performance. © 2024, CC BY.

关键词： Image segmentation

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Identifying driver genes in cancer based on Pareto optimality consensus

Identifying driver genes in cancer based on Pareto optimalit...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Deng, Zheng Wu, Jingli Chen, Xiaorong Li, Gaoshi Guangxi Normal University College of Computer Science Information Technology Guilin China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Wuzhou University Guangxi Key Laboratory of Machine Vision Intelligent Control Wuzhou China

ISBN: (纸本)9781665468190

An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases. © 2022 IEEE.

关键词： Gene expression

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ActMAD: Activation Matching to Align Distributions for Test-Time-Training

arXiv

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arXiv 2022年

作者： Mirza, Muhammad Jehanzeb Soneira, Pol Jané Lin, Wei Kozinski, Mateusz Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision TU Graz Austria Christian Doppler Laboratory for Embedded Machine Learning Institute of Control Systems KIT Germany Christian Doppler Laboratory for Semantic 3D Computer Vision

Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture- and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance. © 2022, CC BY.

关键词： Chemical activation

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Defending Poisoning Attacks in Federated learning via Loss Value Normal Distribution

Defending Poisoning Attacks in Federated Learning via Loss V...

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International Conference on computer Supported Cooperative Work in Design

作者： Fei Han Yao Zhang Meng Zhao Engineering Research Center of Learning-Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of Technology Tianjin China

As an emerging distributed machine learning paradigm, federated learning (FL) only shares model updates between clients and servers, and training data are secured on each client’s device. Due to local data privacy, FL is vulnerable to poisoning attacks, which causes serious degradation in the model performance. In this paper, we analyze the targeted poisoning attacks of FL and reveal that when the data held by each client of FL is independent and identically distributed (iid), the loss of all non-poisoned models on some clean dataset agrees with a normal distribution (ND), while the loss value of the poisoned model deviates from this distribution. Based on this observation, we propose a method named ND_defense to defend FL targeted poisoning attacks. We set a small clean test dataset on the server, compute the loss of each client model on it, detect the abnormal ones with the proposed loss-value-based inspection algorithm, and then only aggregate the benign clients’ models to get the global model. We conducted experiments on three benchmark datasets and compared several state-of-the-art defense methods. The results show that our NDdefense is effective in defending against targeted poisoning attacks.

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MATE: Masked Autoencoders are Online 3D Test-Time Learners

MATE: Masked Autoencoders are Online 3D Test-Time Learners

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International Conference on computer vision (ICCV)

作者： M. Jehanzeb Mirza Inkyu Shin Wei Lin Andreas Schriebl Kunyang Sun Jaesung Choe Mateusz Kozinski Horst Possegger In So Kweon Kuk-Jin Yoon Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Korea Advanced Institute of Science and Technology (KAIST) South Korea Southeast University China

Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: a large portion of each test point cloud is removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. We show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, making it extremely lightweight. Our experiments show that MATE also achieves competitive performance by adapting sparsely on the test data, which further reduces its computational overhead, making it ideal for real-time applications.

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A single 2D pose with context is worth hundreds for 3D human pose estimation 23

A single 2D pose with context is worth hundreds for 3D human...

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

作者： Qitao Zhao Ce Zheng Mengyuan Liu Chen Chen Robotics Institute Carnegie Mellon University Center for Research in Computer Vision University of Central Florida Key Laboratory of Machine Perception Peking University Shenzhen Graduate School

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for improved accuracy, which incurs performance saturation, intractable computation and the non-causal problem. This can be attributed to their inherent inability to perceive spatial context as plain 2D joint coordinates carry no visual cues. To address this issue, we propose a straightforward yet powerful solution: leveraging the readily available intermediate visual representations produced by off-the-shelf (pre-trained) 2D pose detectors - no finetuning on the 3D task is even needed. The key observation is that, while the pose detector learns to localize 2D joints, such representations (e.g., feature maps) implicitly encode the joint-centric spatial context thanks to the regional operations in backbone networks. We design a simple baseline named Context-Aware PoseFormer to showcase its effectiveness. Without access to any temporal information, the proposed method significantly outperforms its context-agnostic counterpart, PoseFormer [74], and other state-of-the-art methods using up to hundreds of video frames regarding both speed and precision. Project page: ***/ContextAware-PoseFormer

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