检索结果-内蒙古大学图书馆

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jiaxin He Chongyuan Ren Yu Ma Yizhou Jiang Yajie Qin School of Information Science and Technology Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai Shanghai China Shanghai Engineering Research Center of Assistive Devices Shanghai China

With the application of high-density neural probes, a neuron can be detected by multiple adjacent probes, and the traditional single-channel spike sorting is no longer suitable. In this paper, we propose a five-channel weighted real-time spike sorting algorithm based on template-matching to process neural signals recorded by high-density probes. This work uses the signals of the center channel and the adjacent four channels to form a five-channel template by weighting, and employs a modified OSort algorithm with unsupervised learning to update the template. We implemented automatic online spike sorting, and tested it with both ground truth recordings and simulated datasets. The experiments show that our algorithm utilizing the information of adjacent channels has a higher sorting accuracy than traditional single-channel spike sorting. The average sorting accuracy reaches 89%, compared to 78% for single-channel.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Discriminative Gradient Adjustment with Coupled Knowledge Distillation for Class Incremental Learning

Discriminative Gradient Adjustment with Coupled Knowledge Di...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Hao Zhang Yanxu Hu 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 Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China

Class Incremental Learning (CIL) is a promising approach to addressing the catastrophic forgetting problem when learning for new categories. Though recent works based on dynamic architectures achieve convincing performance, data imbalance caused by limited size of memory and compression of the increasingly growing network are challenges to be solved. In this paper, we propose the novel Discriminative Gradient Adjustment (DGA) and Coupled Knowledge Distillation strategy (CKD) for these two challengs. The DGA mitigates the data imbalance problem by designing the loss function with a static global balance factor and a ground-truth-based dynamic factor. The CKD fully utilizes intermediate layers of the dual-branch models by feature-level distillation with moving-average weight updating for network compression. Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate the superiority of our method for CIL.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Segmentation and Semantic Segmentation for Underwater Imagery

Few-Shot Segmentation and Semantic Segmentation for Underwat...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Imran Kabir Shubham Shaurya Vijayalaxmi Maigur Nikhil Thakurdesai Mahesh Latnekar Mayank Raunak David Crandall Md Alimoor Reza College of Information Sciences and Technology Pennsylvania State University PA USA Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Mathematics and Computer Science Drake University Des Moines IA USA

This paper tackles image segmentation problems for underwater environments. First, we introduce a novel under-water animal-centric dataset with dense pixel-level annotations containing diverse fine-grained animal categories to mitigate the lack of diverse categories in the existing benchmarks. Then, we solve two image segmentation tasks using underwater images in this dataset: (i) few-shot segmentation, and (ii) semantic segmentation. For the segmentation task in a few-shot learning framework, we propose a novel attention-guided deep neural network architecture by infusing attention modules in various stages of our proposed network. We systematically explore how the learned attention maps can improve few-shot segmentation performance for underwater imagery. Finally, we assess the semantic segmentation problem on our proposed dataset by benchmarking it with two state-of-the-art semantic segmentation methods. We believe our new problem setup, i.e., few-shot segmentation for underwater environments, will be a valuable addition to the existing underwater semantic segmentation task. We believe our novel dataset will pave the way for developing better algorithms and exploring new research directions for marine robotics and underwater image understanding. We publicly release our dataset and the code to advance image understanding research in underwater environments: https://***/Imran220S/uwsnet.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dual Episodic Sampling and Momentum Consistency Regularization for Unsupervised Few-shot Learning

Dual Episodic Sampling and Momentum Consistency Regularizati...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiaxin Chen Yanxu Hu Meng Shen Andy J. Ma School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Unsupervised Few-shot Learning (UFSL) is a practical approach to adapting knowledge learned from unlabeled data of base classes to novel classes with limited labeled data. Nevertheless, most existing UFSL methods may not learn generalizable features in latter training epochs due to the simplicity of meta-learning tasks constructed by data augmentation. To address this issue, we propose two novel components, namely Dual Episodic Sampling (DES) and Momentum Consistency Regularization (MCR) for UFSL. In the DES, two types of sampling strategies are used to construct harder training tasks with multiple augmentations to generate each pseudo-class of increased diversity. The MCR constrains the consistency of the backbone encoder with its momentum counterpart to learn better generalized features for novel classes. Experimental results on four datasets verify the superiority of our method for unsupervised few-shot image classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

STAU: a SpatioTemporal-Aware Unit for Video Prediction and beyond

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Chang, Zheng Zhang, Xinfeng Wang, Shanshe Ma, Siwei Gao, Wen Chinese Academy of Sciences Institute of Computing Technology Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Peking University National Engineering Research Center of Visual Technology Beijing 100871 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing 100049 China Information Technology R&D Innovation Center of Peking University Shaoxing 312000 China

Video prediction aims to predict future frames by modeling the complex spatiotemporal dynamics in videos. However, most existing methods only model the temporal information and the spatial information for videos in an independent manner but have not fully explored the correlations between both terms. In this paper, we propose a SpatioTemporal-Aware Unit (STAU) for video prediction and beyond by exploring the significant spatiotemporal correlations in videos. On the one hand, the motion-aware attention weights are learned from the spatial states to help aggregate the temporal states in the temporal domain. On the other hand, the appearance-aware attention weights are learned from the temporal states to help aggregate the spatial states in the spatial domain. In this way, the temporal information and the spatial information can be greatly aware of each other in both domains, during which, the spatiotemporal receptive field can also be greatly broadened for more reliable spatiotemporal modeling. Experiments are not only conducted on video prediction tasks (deterministic and stochastic), but also another task beyond video prediction, the early action recognition task. Experimental results show that the proposed STAU can achieve satisfactory performance on all tasks compared with other methods. © 1979-2012 IEEE.

关键词： attention mechanism spatiotemporal correlations Spatiotemporal-aware video prediction and beyond

来源：评论

学校读者我要写书评

暂无评论

Enhancing relation extraction using multi-task learning with SDP evidence

引用

information sciences 2024年 670卷

作者： Wang, Hailin Zhang, Dan Liu, Guisong Huang, Li Qin, Ke Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Sichuan Chengdu China Kash Institute of Electronics and Information Industry Xinjiang Kash China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

Relation extraction (RE) is a crucial subtask of information extraction, which involves recognizing the relation between entity pairs in a sentence. Previous studies have extensively employed syntactic information, notably the shortest dependency path (SDP), to collect word evidence, termed SDP evidence, which gives clues about the given entity pair, thus improving RE. Nevertheless, prevalent transformer-based techniques lack syntactic information and cannot effectively model essential syntactic clues to support relations. This study exerts multi-task learning to address these issues by imbibing an SDP token position prediction task into the RE task. To this end, we introduce SGA, an SDP evidence guiding approach that transfers the SDP evidence into two novel supervisory signal labels: SDP tokens label and SDP matrix label. The former guides the attention modules to assign high attention weights to SDP token positions, emphasizing relational clues. In the meantime, the latter supervises SGA to predict a parameterized asymmetric product matrix among the SDP tokens for RE. Experimental outcomes demonstrate the model's enhanced ability to leverage SDP information, thereby directing attention modules and predicted matrix labels to focus on SDP evidence. Consequently, our proposed approach surpasses existing publicly available optimal baselines across four RE datasets: SemEval2010-Task8, KBP37, NYT, and WebNLG.¹ © 2024 Elsevier Inc.

关键词： Evidence Multi-task learning Relation extraction Shortest dependency path

来源：评论

学校读者我要写书评

暂无评论

Relation Regularized Scene Graph Generation

arXiv

引用

arXiv 2022年

作者： Guo, Yuyu Gao, Lianli Song, Jingkuan Wang, Peng Sebe, Nicu Shen, Heng Tao Li, Xuelong The Future Media Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computing and Information Technology The University of Wollongong NSW2170 Australia The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy Northwestern Polytechnical University Xi'An710072 China

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this observation, in this paper, we propose a Relation Regularized Network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG. Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects. Graph Convolution Networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects. With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs. Extensive experiments are conducted on the Visual Genome dataset for three SGG tasks (i.e., PREDCLS, SGSLS, and SGDET), demonstrating the effectiveness of our proposed method. Ablation studies also verify the key roles of our proposed components in performance improvement. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Context Learning Approach for Text-to-SQL Generation

MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Cont...

引用

Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Yiming Zhan Zhao Li Tong Chen Shiwei Wu Yang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Evay Info Technology Co. Ltd. Jinan China School of Cyberspace Security (School of Cryptology) Hainan University Haikou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In-Context learning serves as a vital component for enhancing Text-to-SQL task performance. However, current in-context learning methodologies for Text-to-SQL applications fail to sufficiently bridge the significant cross-domain correlation gap that exists between few-shot examples and the intended query target. Our approach overcomes these challenges through a novel relevant retrieval-based in-context learning method, which operates across three distinct granularity levels: (1) semantic similarity retrieval for analogous questions, (2) query kernel entity matching for retrieving strongly relevant questions and (3) character-level similarity analysis for fine-grained question retrieval. Additionally, we introduce a novel Object-Relational Mapping (ORM)-based database prompt and a post cell value linking method that effectively describes the schema while avoiding error propagation common in traditional schema linking approaches. Our experimental evaluations conducted on the benchmark datasets BIRD and Spider reveal that our approach delivers strong performance, comparable to state-of-the-art methods.

关键词： Learning systems Structured Query Language Databases Large language models Computational modeling Semantics Benchmark testing Birds Business intelligence Standards

来源：评论

学校读者我要写书评

暂无评论

Exploring the GLIDE model for Human Action-effect Prediction

arXiv

引用

arXiv 2022年

作者： Li, Fangjun Hogg, David C. Cohn, Anthony G. School of Computing University of Leeds United Kingdom Luzhong Institute of Safety Qingdao University of Science and Technology China College of Electronic and Information Engineering Tongji University China School of Mechanical and Electrical Engineering Qingdao University of Science and Technology China School of Control Science and Engineering Shandong University China

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing this task. GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text. Our idea is to mask-out a region of the input image where the effect of the action is expected to occur. GLIDE is then used to inpaint the masked region conditioned on the required action. In this way, the resulting image has the same background context as the input image, updated to show the effect of the action. We give qualitative results from experiments using the EPIC dataset of ego-centric videos labelled with actions. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints

arXiv

引用

arXiv 2022年

作者： Liu, Tianxiang Pong, Ting Kei Takeda, Akiko Department of Mathematical and Computing Science School of Computing Tokyo Institute of Technology Tokyo Japan Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Department of Creative Informatics Graduate School of Information Science and Technology University of Tokyo Tokyo Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

We consider a class of sparsity-inducing optimization problems whose constraint set is regularizer-compatible, in the sense that, the constraint set becomes easy-to-project-onto after a coordinate transformation induced by the sparsity-inducing regularizer. Our model is general enough to cover, as special cases, the ordered LASSO model in [21] and its variants with some commonly used nonconvex sparsity-inducing regularizers. The presence of both the sparsity-inducing regularizer and the constraint set poses challenges on the design of efficient algorithms. In this paper, by exploiting absolute-value symmetry and other properties in the sparsity-inducing regularizer, we propose a new algorithm, called the Doubly Majorized Algorithm (DMA), for this class of problems. The DMA makes use of projections onto the constraint set after the coordinate transformation in each iteration, and hence can be performed efficiently. Without invoking any commonly used constraint qualification conditions such as those based on horizon subdifferentials, we show that any accumulation point of the sequence generated by DMA is a so-called ψopt-stationary point, a new notion of stationarity we define as inspired by the notion of L-stationarity in [2,3]. We also show that any global minimizer of our model has to be a ψopt-stationary point, again without imposing any constraint qualification conditions. Finally, we illustrate numerically the performance of DMA on solving variants of ordered LASSO with nonconvex regularizers. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：