检索结果-南通市图书馆

Semantic segmentation via pixel-to-center similarity calculation

CAAI Transactions on Intelligence Technology 2024年第1期9卷 87-100页

作者： Dongyue Wu Zilin Guo Aoyan Li Changqian Yu Nong Sang Changxin Gao National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Meituan Inc. BeijingChina

Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel representations.However,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinction in the same scene could be small,resulting in the limited performance to distinguish different classes in each scene.The authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class centers.Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class center.Thus,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers.Under this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers.The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different scenes.Specially designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center similarity.Finally,the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction.Extensive experiments demonstrate that our model performs favourably against the state-of-the-art methods.

关键词： computer vision deep neural networks image segmentation scene understanding

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Fast Panoptic Segmentation Network for Self-Driving scene understanding

引用

Computer Systems Science & Engineering 2022年第10期43卷 27-43页

作者： Abdul Majid Sumaira Kausar Samabia Tehsin Amina Jameel Department of Computer Science Bahiria UniversityIslamabadPakistan Department of Software Engineering Bahiria UniversityKarachiPakistan

In recent years,a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies.The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to.So it is required to combine segmentation and detection in a single framework.Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application.scene understanding systems typically involves detection and segmentation of different natural and manmade things.A lot of research has been performed in recent years,mostly with a focus on things(a well-defined objects that has shape,orientations and size)with a less focus on stuff classes(amorphous regions that are unclear and lack a shape,size or other characteristics Stuff region describes many aspects of scene,like type,situation,environment of scene etc.and hence can be very helpful in scene understanding.Existing methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time,accuracy and robustness for varying level of scene complexity.A robust scene understanding method has to effectively deal with imbalanced distribution of classes,overlapping objects,fuzzy object boundaries and poorly localized objects.The proposed method presents Panoptic Segmentation on Cityscapes ***-V2 is used as a backbone for feature extraction that is pre-trained on ***-V2 with state-of-art encoder-decoder architecture of DeepLabV3+with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and robust.Very promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and reliable way.

关键词： Panoptic segmentation instance segmentation semantic segmentation deep learning computer vision scene understanding autonomous applications atrous convolution

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Survey of scene understanding by Event Reasoning in Autonomous Driving

引用

International Journal of Automation and computing 2018年第3期15卷 249-266页

作者： Jian-Ru Xue Jian-Wu Fang Pu Zhang Institute of Artificial Intelligence and Robotics Xi'an Jiaotong UniversityXi'an 710049China Chang'an University Xi'an 710064China

Realizing autonomy is a hot research topic for automatic vehicles in recent years. For a long time, most of the efforts to this goal concentrate on understanding the scenes surrounding the ego-vehicle（autonomous vehicle itself）. By completing lowlevel vision tasks, such as detection, tracking and segmentation of the surrounding traffic participants, e.g., pedestrian, cyclists and vehicles, the scenes can be interpreted. However, for an autonomous vehicle, low-level vision tasks are largely insufficient to give help to comprehensive scene understanding. What are and how about the past, the on-going and the future of the scene participants？ This deep question actually steers the vehicles towards truly full automation, just like human beings. Based on this thoughtfulness, this paper attempts to investigate the interpretation of traffic scene in autonomous driving from an event reasoning view. To reach this goal, we study the most relevant literatures and the state-of-the-arts on scene representation, event detection and intention prediction in autonomous driving. In addition, we also discuss the open challenges and problems in this field and endeavor to provide possible solutions.

关键词： Autonomous vehicle scene understanding event reasoning intention prediction scene representation.

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Lighting virtual objects in a single image via coarse scene understanding

引用

Science China(Information Sciences) 2014年第9期57卷 58-71页

作者： CHEN XiaoWu JIN Xin WANG Ke State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang University

Achieving convincing visual consistency between virtual objects and a real scene mainly relies on the lighting effects of virtual-real composition scenes.The problem becomes more challenging in lighting virtual objects in a single real image.Recently,scene understanding from a single image has made great progress.The estimated geometry,semantic labels and intrinsic components provide mostly coarse information,and are not accurate enough to re-render the whole scene.However,carefully integrating the estimated coarse information can lead to an estimate of the illumination parameters of the real scene.We present a novel method that uses the coarse information estimated by current scene understanding technology to estimate the parameters of a ray-based illumination model to light virtual objects in a real scene.Our key idea is to estimate the illumination via a sparse set of small 3D surfaces using normal and semantic constraints.The coarse shading image obtained by intrinsic image decomposition is considered as the irradiance of the selected small surfaces.The virtual objects are illuminated by the estimated illumination parameters.Experimental results show that our method can convincingly light virtual objects in a single real image,without any pre-recorded 3D geometry,reflectance,illumination acquisition equipment or imaging information of the image.

关键词： illumination estimation lighting virtual object single image scene understanding photo editing

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

ARM3D:Attention-based relation module for indoor 3D object detection

引用

Computational Visual Media 2022年第3期8卷 395-414页

作者： Yuqing Lan Yao Duan Chenyi Liu Chenyang Zhu Yueshan Xiong Hui Huang Kai Xu College of Computer National University of Defense TechnologyChangsha 410073China Shenzhen University Shenzhen 518061China

Relation contexts have been proved to be useful for many challenging vision tasks.In the field of3D object detection,previous methods have been taking the advantage of context encoding,graph embedding,or explicit relation reasoning to extract relation contexts.However,there exist inevitably redundant relation contexts due to noisy or low-quality proposals.In fact,invalid relation contexts usually indicate underlying scene misunderstanding and ambiguity,which may,on the contrary,reduce the performance in complex scenes.Inspired by recent attention mechanism like Transformer,we propose a novel 3D attention-based relation module(ARM3D).It encompasses objectaware relation reasoning to extract pair-wise relation contexts among qualified proposals and an attention module to distribute attention weights towards different relation contexts.In this way,ARM3D can take full advantage of the useful relation contexts and filter those less relevant or even confusing contexts,which mitigates the ambiguity in detection.We have evaluated the effectiveness of ARM3D by plugging it into several state-of-the-art 3D object detectors and showing more accurate and robust detection results.Extensive experiments show the capability and generalization of ARM3D on 3D object detection.Our source code is available at https://***/lanlan96/ARM3D.

关键词： attention mechanism scene understanding relational reasoning 3D indoor object detection

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A semantic-centered cloud control framework for autonomous unmanned system

引用

Journal of Systems Engineering and Electronics 2022年第4期33卷 771-784页

作者： PANG Weijian LI Hui MA Xinyi ZHANG Hailin Academy of Military Sciences Beijing 100091China Beijing Aeronautical Engineering Technology Research Center Beijing 100076China Air Forces Command College Beijing 100097China

Rich semantic information in natural language increases team efficiency in human collaboration, reduces dependence on high precision data information, and improves adaptability to dynamic environment. We propose a semantic centered cloud control framework for cooperative multi-unmanned ground vehicle(UGV) system. Firstly, semantic modeling of task and environment is implemented by ontology to build a unified conceptual architecture, and secondly, a scene semantic information extraction method combining deep learning and semantic web rule language(SWRL) rules is used to realize the scene understanding and task-level cloud task cooperation. Finally, simulation results show that the framework is a feasible way to enable autonomous unmanned systems to conduct cooperative tasks.

关键词： scene understanding cloud control ontology autonomous cooperation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Multimodal feature fusion based on object relation for video captioning

引用

CAAI Transactions on Intelligence Technology 2023年第1期8卷 247-259页

作者： Zhiwen Yan Ying Chen Jinlong Song Jia Zhu School of Computing Science South China Normal UniversityGuangzhouChina Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhuaChina

Video captioning aims at automatically generating a natural language caption to describe the content of a video.However,most of the existing methods in the video captioning task ignore the relationship between objects in the video and the correlation between multimodal features,and they also ignore the effect of caption length on the task.This study proposes a novel video captioning framework(ORMF)based on the object relation graph and multimodal feature fusion.ORMF uses the similarity and Spatio-temporal relationship of objects in video to construct object relation features graph and introduce graph convolution network(GCN)to encode the object relation.At the same time,ORMF also constructs a multimodal features fusion network to learn the relationship between different modal features.The multimodal feature fusion network is used to fuse the features of different modals.Furthermore,the proposed model calculates the length loss of the caption,making the caption get richer information.The experimental results on two public datasets(Microsoft video captioning corpus[MSVD]and Microsoft research-video to text[MSR-VTT])demonstrate the effectiveness of our method.

关键词： approaches deep learning multimodel scene understanding video analysis

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

3D scene graph prediction from point clouds

引用

Virtual Reality & Intelligent Hardware 2022年第1期4卷 76-88页

作者： Fanfan WU Feihu YAN Weimin SHI Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background In this study,we propose a novel 3D scene graph prediction approach for scene understanding from point clouds.Methods It can automatically organize the entities of a scene in a graph,where objects are nodes and their relationships are modeled as edges.More specifically,we employ the DGCNN to capture the features of objects and their relationships in the scene.A Graph Attention Network(GAT)is introduced to exploit latent features obtained from the initial estimation to further refine the object arrangement in the graph structure.A one loss function modified from cross entropy with a variable weight is proposed to solve the multi-category problem in the prediction of object and predicate.Results Experiments reveal that the proposed approach performs favorably against the state-of-the-art methods in terms of predicate classification and relationship prediction and achieves comparable performance on object classification prediction.Conclusions The 3D scene graph prediction approach can form an abstract description of the scene space from point clouds.

关键词： scene understanding 3D scene graph Point cloud DGCNN GAT

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Robust Counting in Overcrowded scenes Using Batch-Free Normalized Deep ConvNet

引用

Computer Systems Science & Engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Comprehensive Review of Group Activity Recognition in Videos

引用

International Journal of Automation and computing 2021年第3期18卷 334-350页

作者： Li-Fang Wu Qi Wang Meng Jian Yu Qiao Bo-Xuan Zhao College of Information and Communication Engineering Beijing University of TechnologyBeijing 100124China Beijing Municipal Key Lab of Computation Intelligence and Intelligent Systems Beijing University of TechnologyBeijing 100124China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China

Human group activity recognition(GAR)has attracted significant attention from computer vision researchers due to its wide practical applications in security surveillance,social role understanding and sports video analysis.In this paper,we give a comprehensive overview of the advances in group activity recognition in videos during the past 20 years.First,we provide a summary and comparison of 11 GAR video datasets in this field.Second,we survey the group activity recognition methods,including those based on handcrafted features and those based on deep learning networks.For better understanding of the pros and cons of these methods,we compare various models from the past to the present.Finally,we outline several challenging issues and possible directions for future research.From this comprehensive literature review,readers can obtain an overview of progress in group activity recognition for future studies.

关键词： Group activity recognition(GAR) human activity recognition scene understanding video analysis computer vision

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：