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《Information processing & management》2023,60(2):103207
Visual Question Answering (VQA) requires reasoning about the visually-grounded relations in the image and question context. A crucial aspect of solving complex questions is reliable multi-hop reasoning, i.e., dynamically learning the interplay between visual entities in each step. In this paper, we investigate the potential of the reasoning graph network on multi-hop reasoning questions, especially over 3 “hops.” We call this model QMRGT: A Question-Guided Multi-hop Reasoning Graph Network. It constructs a cross-modal interaction module (CIM) and a multi-hop reasoning graph network (MRGT) and infers an answer by dynamically updating the inter-associated instruction between two modalities. Our graph reasoning module can apply to any multi-modal model. The experiments on VQA 2.0 and GQA (in fully supervised and O.O.D settings) datasets show that both QMRGT and pre-training V&L models+MRGT lead to improvement on visual question answering tasks. Graph-based multi-hop reasoning provides an effective signal for the visual question answering challenge, both for the O.O.D and high-level reasoning questions. 相似文献
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排序是信息检索、数据挖掘以及社会网络分析的基础工作之一。 在线社交网络和社 会媒体的快速发展积累了大量的图数据——由表示实体的节点和表示实体间关系的连边构 成。 图数据中节点之间连接关系复杂, 通常缺少显式的全序结构, 使得图排序在图数据分析 中显得尤为重要。 图排序算法主要包括 2 大类, 面向节点中心度的图排序算法和面向节点集 合多样性的图排序算法。 与传统的图排序不同 , 多样性图排序考虑排序和聚类的融合, 体现 为节点集合对网络整体的覆盖程度。 近年来, 多样性图排序得到了广泛的关注, 取得了一系 列研究进展,研究成果成功应用到了搜索结果排序、文档自动摘要、信息推荐系统和影响最大 化等诸多场景中。 文章评述了多样性图排序的研究现状及主要进展, 将现有的多样性图排序 方法按照研究思路的不同分为边际效益最大化、竞争随机游走、聚类与排序互增强 3 类, 分别 评述了每类方法的优势和不足。 最后指出 , 设计有效的评价指标和标准测试集、克服多样性 图排序面临的精度和速度的矛盾等是多样性图排序未来的研究重点。 相似文献
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《Information processing & management》2022,59(4):102953
Aspect-based sentiment analysis technologies may be a very practical methodology for securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt the recurrent neural network or attention-based neural network methods to infer aspect sentiment using opinion context terms and sentence dependency trees. However, due to a sentence often having multiple aspects sentiment representation, these models are hard to achieve satisfactory classification results. In this paper, we discuss these problems by encoding sentence syntax tree, words relations and opinion dictionary information in a unified framework. We called this method heterogeneous graph neural networks (Hete_GNNs). Firstly, we adopt the interactive aspect words and contexts to encode the sentence sequence information for parameter sharing. Then, we utilized a novel heterogeneous graph neural network for encoding these sentences’ syntax dependency tree, prior sentiment dictionary, and some part-of-speech tagging information for sentiment prediction. We perform the Hete_GNNs sentiment judgment and report the experiments on five domain datasets, and the results confirm that the heterogeneous context information can be better captured with heterogeneous graph neural networks. The improvement of the proposed method is demonstrated by aspect sentiment classification task comparison. 相似文献
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《Information processing & management》2023,60(3):103283
Text-enhanced and implicit reasoning methods are proposed for answering questions over incomplete knowledge graph (KG), whereas prior studies either rely on external resources or lack necessary interpretability. This article desires to extend the line of reinforcement learning (RL) methods for better interpretability and dynamically augment original KG action space with additional actions. To this end, we propose a RL framework along with a dynamic completion mechanism, namely Dynamic Completion Reasoning Network (DCRN). DCRN consists of an action space completion module and a policy network. The action space completion module exploits three sub-modules (relation selector, relation pruner and tail entity predictor) to enrich options for decision making. The policy network calculates probability distribution over joint action space and selects promising next-step actions. Simultaneously, we employ the beam search-based action selection strategy to alleviate delayed and sparse rewards. Extensive experiments conducted on WebQSP, CWQ and MetaQA demonstrate the effectiveness of DCRN. Specifically, under 50% KG setting, the Hits@1 performance improvements of DCRN on MetaQA-1H and MetaQA-3H are 2.94% and 1.18% respectively. Moreover, under 30% and 10% KG settings, DCRN prevails over all baselines by 0.9% and 1.5% on WebQSP, indicating the robustness to sparse KGs. 相似文献
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根据城市排水系统的设置要求,基于图论网络原理,分析城市排水管网的分布。利用排水管道重力单向流动的特点,建立了以汇水区域为源点,收水口、检查井和出水口为中间节点,河流为汇点,赋管网以权重的有向网络模型。利用图论中网络流的最大流的值为最小割的容量来计算排水管网的排水量。将求解排水量转化为一个运筹规划问题。并以此评估现有管网的整体排放能力。计算中使用Ford—Fulkerson算法来计算网络最大流。 相似文献
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Convolutional neural network (CNN) and its variants have led to many state-of-the-art results in various fields. However, a clear theoretical understanding of such networks is still lacking. Recently, a multilayer convolutional sparse coding (ML-CSC) model has been proposed and proved to equal such simply stacked networks (plain networks). Here, we consider the initialization, the dictionary design and the number of iterations to be factors in each layer that greatly affect the performance of the ML-CSC model. Inspired by these considerations, we propose two novel multilayer models: the residual convolutional sparse coding (Res-CSC) model and the mixed-scale dense convolutional sparse coding (MSD-CSC) model. They are closely related to the residual neural network (ResNet) and the mixed-scale (dilated) dense neural network (MSDNet), respectively. Mathematically, we derive the skip connection in the ResNet as a special case of a new forward propagation rule for the ML-CSC model. We also find a theoretical interpretation of dilated convolution and dense connection in the MSDNet by analyzing the MSD-CSC model, which gives a clear mathematical understanding of each. We implement the iterative soft thresholding algorithm and its fast version to solve the Res-CSC and MSD-CSC models. The unfolding operation can be employed for further improvement. Finally, extensive numerical experiments and comparison with competing methods demonstrate their effectiveness. 相似文献
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[目的/意义]大数据时代智慧图书馆用户需要精确智能的检索工具,而移动视觉搜索技术能够满足用户以视觉资源数据为中心的检索需求。[方法/过程]文章在分析国内外基于深度学习的视觉资源识别技术的研究基础之上,构建了基于深度学习的智慧图书馆移动视觉搜索服务模式模型,并设计了模型的工作流程,最后对基于深度学习的智慧图书馆移动视觉搜索系统的发展趋势进行了展望。[结果/结论]将深度学习融入智慧图书馆移动视觉搜索系统中,可以整合多源异构视觉数据、贴合用户个性化偏好以及提高移动视觉搜索系统性能。 相似文献
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所谓"本格推理",就是逻辑至上,严格遵循理性的原则。"本格推理"是侦探小说的一个主要派别。"本格"原为日文词,本格派又可称为古典派或传统派。以推理解谜为主要走向,是侦探推理小说的主流。本文以谢鑫创作的"课外侦探组"系列少年侦探小说为例,阐述"本格推理"的表现手法在中国当代少年侦探小说中的运用及其成效。 相似文献
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《Information processing & management》2023,60(3):103264
Multimodal relation extraction is a critical task in information extraction, aiming to predict the class of relations between head and tail entities from linguistic sequences and related images. However, the current works are vulnerable to less relevant visual objects detected from images and are not able to sufficiently fuse visual information into text pre-trained models. To overcome these problems, we propose a Two-Stage Visual Fusion Network (TSVFN) that employs the multimodal fusion approach in vision-enhanced entity relation extraction. In the first stage, we design multimodal graphs, whose novelty lies mainly in transforming the sequence learning into the graph learning. In the second stage, we merge the transformer-based visual representation into the text pre-trained model by a multi-scale cross-model projector. Specifically, two multimodal fusion operations are implemented inside the pre-trained model respectively. We finally accomplish deep interaction of multimodal multi-structured data in two fusion stages. Extensive experiments are conducted on a dataset (MNRE), our model outperforms the current state-of-the-art method by 1.76%, 1.52%, 1.29%, and 1.17% in terms of accuracy, precision, recall, and F1 score, respectively. Moreover, our model also achieves excellent results under the condition of fewer samples. 相似文献
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[目的/意义]现代社会已进入大数据时代,基于用户画像的智能信息服务深刻地改变了人们的生活,对图书馆领域产生了重要影响,研究用户画像对社会的发展具有重要作用。[方法/过程]以CNKI中国学术文献网络出版总库中的用户画像文献作为研究对象,使用CiteSpace绘制可视化知识图谱,进行关键词分析,揭示我国用户画像研究的时间分布、学科领域、主题演变和研究热点问题。[结果/结论]我国用户画像研究划分为初始阶段、起步阶段和发展阶段,从2015年开始快速发展,但基础理论研究较少,研究成果尚未形成体系;用户画像研究文献从最初的计算机和电子商务等学科领域逐渐向管理学、经济学、人文社科领域发展,呈现出明显的跨学科特征;大数据构成了用户画像研究的数据基础,随着计算机和信息网络技术的发展,用户画像研究和实践应用不断发展,图书情报与数字图书馆是用户画像研究的重要领域;研究热点包括基础理论、核心技术、实践应用和基础数据四方面内容。 相似文献
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提出一种基于网络中心性的计算机网络脆弱性评估方法.首先基于通用脆弱性评分系统,对攻击者利用脆弱性攻击所花费的代价进行量化评估,根据评估结果对脆弱性攻击图进行最小攻击代价路径分析.引入网络中心性理论,采用攻击图节点的介数和节点连通度相结合的方法,对攻击图的节点关键程度进行量化分析,判断对网络安全产生关键影响的脆弱性,为计算机网络的安全优化提供依据. 相似文献
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《Information processing & management》2023,60(2):103174
The majority of currently available entity alignment (EA) solutions primarily rely on structural information to align entities, which is biased and disregards additional multi-source information. To compensate for inadequate structural details, this article suggests the SKEA framework, which is a simple but flexible framework for Entity Alignment with cross-modal supervision of Supporting Knowledge. We employ a relational aggregate network to specifically utilize the details about the entity and its neighbors. To overcome the limitations of relational features, two multi-modal encode modules are being used to extract visual and textural information. A new set of potential aligned entity pairs are generated by SKEA in each iteration using the knowledge of two reference modalities, which can enhance the model’s supervision. It is important to note that the supporting information used in our framework does not participate in the network’s backpropagation, which considerably improves efficiency and differs dramatically from earlier work. In comparison to existing baselines, experiments demonstrate that our proposed framework can incorporate multi-aspect information efficiently and enable supervisory signals from other modalities to transmit to entities. The maximum performance improvement of 5.24% indicates our suggested framework’s superiority, especially for sparse KGs. 相似文献
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《Information processing & management》2023,60(4):103375
As one of the challenging cross-modal tasks, video question answering (VideoQA) aims to fully understand video content and answer relevant questions. The mainstream approach in current work involves extracting appearance and motion features to characterize videos separately, ignoring the interactions between them and with the question. Furthermore, some crucial semantic interaction details between visual objects are overlooked. In this paper, we propose a novel Relation-aware Graph Reasoning (ReGR) framework for video question answering, which first combines appearance–motion and location–semantic multiple interaction relations between visual objects. For the interaction between appearance and motion, we design the Appearance–Motion Block, which is question-guided to capture the interdependence between appearance and motion. For the interaction between location and semantics, we design the Location–Semantic Block, which utilizes the constructed Multi-Relation Graph Attention Network to capture the geometric position and semantic interaction between objects. Finally, the question-driven Multi-Visual Fusion captures more accurate multimodal representations. Extensive experiments on three benchmark datasets, TGIF-QA, MSVD-QA, and MSRVTT-QA, demonstrate the superiority of our proposed ReGR compared to the state-of-the-art methods. 相似文献
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本文论述了服务型统计的概念。尽管统计服务对象是多元化的,但“用数据说话,用数据服务”依然是统计工作的主题,从而通过论述当前对统计数据要求的新挑战和统计应对工作的重点,尝试提高数据质量、融合资源、增强匹配、充分应用数据资料、拓宽服务领域应对统计数据来自环境、需求、匹配、责任等方面新挑战。 相似文献
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魏二玲 《中国科学院研究生院学报》2004,21(4):451-453
20世纪 70年代 ,刘彦佩通过构造图的辅助图 ,得到判定图是否平面的充分必要条件 .图的标号三元图刻画了图的内在结构 ,也可以刻画图是否平面 .证明了图的辅助图是否平衡以及标号三元图是否平衡是等价的 ;并进一步指出 ,用辅助图来判定要优于用标号三元图 . 相似文献
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[研究目的]高质量专利对促进专利转化、技术追踪和战略布局十分重要,面对海量专利数据,如何准确高效自动识别高质量专利,为开展后续专利投资融资、产业转型等专利工作做基础铺垫,成为当前重要研究问题。[研究方法]以国家知识产权局受理的申请专利为研究对象,使用专利维持年限表征专利质量,提取专利数字特征并嵌入专利文本特征生成的专利-核心词汇网络,搭建图卷积网络模型自动识别高质量专利。[研究结论]目前针对专利质量的研究专注于挖掘专利数字特征而忽视专利文本特征,该方案在高质量专利自动识别过程中使用专利数字特征与文本特征,对当前专利质量研究做出补充。此外,所提方案可在专家标注少量专利文档情况下完成专利质量识别任务,解决现有专利质量标签标注方案无法全面衡量专利质量的局限。同时,将图卷积网络扩展到专利背景下的质量识别领域,为专利质量研究提供崭新框架,实验结果也显示方案具有较高实践价值。 相似文献