检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
2 篇 教育学
- 2 篇 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

34 篇 univ sci & techn...
34 篇 tsinghua univ pe...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
17 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
25 篇 radu timofte
19 篇 van gool luc
15 篇 loy chen change
15 篇 lei lei
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
13 篇 pan jinshan
13 篇 li chongyi
13 篇 qiao yu
12 篇 chen chen
12 篇 marcos v. conde
12 篇 liu shuai
12 篇 chen wei-ting
11 篇 wangmeng zuo
11 篇 yu qiao
10 篇 wei dong
10 篇 zhou shangchen

语言

3,318 篇 英文
2 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2221-2230 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

AdvDenoise: Fast Generation Framework of Universal and Robust Adversarial Patches Using Denoise

AdvDenoise: Fast Generation Framework of Universal and Robus...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jing Li Zigan Wang Jinliang Li School of Economics and Management Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Adversarial patch attacks, which can mislead deep learning models and the human eye in both the digital and physical domains, have led to a trust crisis. Traditional approaches to generating powerful attack patches require extensive, multi-scenario data, but suffer from slow search speeds in adversarial gradient space, resulting in low global attack success rates and high costs. Especially high resource-consuming attack methods are not sufficient to pose sufficient threats, which leads to the vulnerability of defense. To address these challenges, we present a novel framework AdvDenoise to generate universal adversarial patches fast and robustly using denoise. Concretely, we leverage the power of denoising diffusion probabilistic models to craft or optimize these patches, deviating from traditional pure gradient-based methods. We conduct comprehensive experiments on both pre-trained convolutional neural networks and vision transformer detectors, evaluating our method on standard benchmarks as well as in simulated real-world physical settings. The results demonstrate that our framework outperforms strong baselines, achieving higher attack success rates, better transferability across models, and improved robustness to transformations while maintaining visual realism and computational efficiency. When our method’s performance approaches the state-of-the-art, the total time required to generate 100-shots adversarial patches is substantially lower than the state-of-the-art methods, with a remarkable 48.15% reduction in time complexity. The code and examples are publicly available at https://***/advdenoise/advdenoise.

关键词： computer vision Visualization Computational modeling Noise reduction Diffusion models Transformers Robustness

来源：评论

学校读者我要写书评

暂无评论

SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

SegFormer3D: an Efficient Transformer for 3D Medical Image S...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shehan Perera Pouyan Navard Alper Yilmaz Photogrammetric Computer Vision Lab The Ohio State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The adoption of vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence. In response to these challenges and to demonstrate that lightweight models are a valuable area of research in 3D medical imaging, we present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. Additionally, SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks. The proposed memory efficient Transformer preserves the performance characteristics of a significantly larger model in a compact design. SegFormer3D democratizes deep learning for 3D medical image segmentation by offering a model with 33× less parameters and a 13× reduction in GFLOPS compared to the current state-of-the-art (SOTA). We benchmark SegFormer3D against the current SOTA models on three widely used datasets Synapse, BRaTs, and ACDC, achieving competitive results. Code: https://***/OSUPCVLab/***

关键词： Training Image segmentation Solid modeling Three-dimensional displays Computational modeling computer architecture Transformers

来源：评论

学校读者我要写书评

暂无评论

ATOM: Attention Mixer for Efficient Dataset Distillation

ATOM: Attention Mixer for Efficient Dataset Distillation

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Samir Khaki Ahmad Sajedi Kai Wang Lucy Z. Liu Yuri A. Lawryshyn Konstantinos N. Plataniotis University of Toronto National University of Singapore Royal Bank of Canada (RBC)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real dataset. These approaches ultimately aim to attain test accuracy levels akin to those achieved by models trained on the entirety of the original dataset. Previous studies in feature and distribution matching have achieved significant results without incurring the costs of bi-level optimization in the distillation process. Despite their convincing efficiency, many of these methods suffer from marginal downstream performance improvements, limited distillation of contextual information, and subpar cross-architecture generalization. To address these challenges in dataset distillation, we propose the ATtentiOn Mixer (ATOM) module to efficiently distill large datasets using a mixture of channel and spatial-wise attention in the feature matching process. Spatial-wise attention helps guide the learning process based on consistent localization of classes in their respective images, allowing for distillation from a broader receptive field. Meanwhile, channel-wise attention captures the contextual information associated with the class itself, thus making the synthetic image more informative for training. By integrating both types of attention, our ATOM module demonstrates superior performance across various computer vision datasets, including CIFAR10/100 and Tiny-Imagenet. Notably, our method significantly improves performance in scenarios with a low number of images per class, thereby enhancing its potential. Furthermore, we maintain the improvement on cross-architectures and applications such as neural architecture search.

关键词： Training Location awareness computer vision Costs Atomic layer deposition Performance gain Transformers

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Stereo Image Super-Resolution: Metho...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Cheng, Ming Ma, Haoyu Ma, Qiufang Sun, Xiaopeng Zhao, Shijie Sheng, Xuhan Ding, Yukan Sun, Ming Wen, Xing Zhang, Dafeng Li, Jia Wang, Fan Xie, Zheng He, Zongyao Qiu, Zidian Pan, Zilin Zhan, Zhihao Xian, Xingyuan Jin, Zhi Zhou, Yuanbo Deng, Wei Nie, Ruofeng Zhang, Jiajun Gao, Qinquan Tong, Tong Zhang, Kexin Zhang, Junpei Peng, Rui Ma, Yanbiao Jiao, Licheng Bai, Haoran Kong, Lingshun Pan, Jinshan Dong, Jiangxin Tang, Jinhui Cao, Pu Huang, Tianrui Yang, Lu Song, Qing Chen, Bingxin He, Chunhua Chen, Meiyun Guo, Zijie Luo, Shaojuan Cao, Chengzhi Wang, Kunyu Zhang, Fanrui Zhang, Qiang Mehta, Nancy Murala, Subrahmanyam Dudhane, Akshay Wang, Yujin Li, Lingen Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Chen, Junyang Li, Hao Shi, Yukai Yang, Zhijing Zou, Wenbin Zhang, Yunchen Jiang, Mingchao Yu, Zhongxin Tan, Ming Gao, Hongxia Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Chen, Jingxiang Yang, Bo Zhang, Xisheryl Li, Chenghua Yuan, Weijun Li, Zhan Deng, Ruting Zeng, Jintao Mahajan, Pulkit Mistry, Sahaj Chatterjee, Shreyas Jakhetiya, Vinit Subudhi, Badri Jaiswal, Sunil Zhang, Zhao Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Wang, Bo Li, Gen Li, Aijin Sun, Lei Chen, Ke Tang, Congling Li, Yunzhe Chiang, Yuan-Chun Chen, Yi-Chung Huang, Zhi-Kai Yang, Hao-Hsiang Chen, I-Hsiang Kuo, Sy-Yen Wang, Yiheng Zhu, Gang Yang, Xingyi Liu, Songhua Jing, Yongcheng Hu, Xingyu Song, Jianwen Sun, Changming Sowmya, Arcot Park, Seung Ho Lei, Xiaoyan Wang, Jingchao Zhai, Chenbo Zhang, Yufei Cao, Weifeng Zhang, Wenlong Aviation University of Air Force China The Shenzhen Campus of Sun Yat-sen University Sun Yatsen University China National University of Defense Technology China Shanghai University China University of Electronic Science and Technology of China China University of Würzburg Germany ETH Zürich Switzerland ByteDance China Kuaishou Technology China Samsung Research China - Beijing China Sun Yat-sen University China Fuzhou University China Imperial Vision Technology Xidian University China Nanjing University of Science and Technology China Beijing University of Posts and Telecommunications China Guangdong University of Technology China The Hong Kong Polytechnic University Hong Kong University of Science and Technology of China China Indian Institute of Technology Ropar India MBZUAI Dubai United Arab Emirates Shanghai Artificial Intelligence Laboratory China Shanghai Jiao Tong University China Assiut University Egypt South China University of Technology China Fujian Normal University China GAC R&D Center Uppsala University Sweden Nanjing University of Information Science and Technology China Chinese Academy of Sciences Institute of Automation China Jinan University China Jiangxi Normal University China Indian Institute of Technology India K-Lens GmbH Hefei University of Technology China McMaster University Canada National Taiwan University Taiwan Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Harbin Institute of Technology China University of New South Wales Australia CSIRO Data61 Australia Seoul National University Korea Republic of Zhengzhou University of Light Industry China

ISBN: (纸本)9798350302493

This paper summarizes the 2nd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of the challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of ×4. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 3 tracks, including one track on distortion (e.g., PSNR) and bicubic degradation, one track on perceptual quality (e.g., LPIPS) and bicubic degradation, as well as another track on real degradations. In total, 175, 93, and 103 participants were successfully registered for each track, respectively. In the test phase, 21, 17, and 12 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR. © 2023 ieee.

关键词： Optical resolving power

来源：评论

学校读者我要写书评

暂无评论

COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

COOD: Combined out-of-distribution detection using multiple ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Laurens E. Hogeweg Rajesh Gangireddy Django Brunink Vincent J. Kalkman Ludo Cornelissen Jacob W. Kamminga Intel Naturalis Biodiversity Center University of Twente

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper we focus on the species recognition task in images, concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1%FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 83.3% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities.

关键词： Image quality computer vision Image recognition Databases conferences Biological system modeling Media

来源：评论

学校读者我要写书评

暂无评论

End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates

End-to-End Neural Network Compression via l1/l2 Regularized ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anshul Nasery Hardik Shah Arun Sai Suggala Prateek Jain University of Washington ETH Zurich Google Research India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model’s Floating Point Operations (FLOPs) via a novel $\frac{{{\ell _1}}}{{{\ell _2}}}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization, and can optimize for on-device latency. Crucially, it is fast and runs in almost the same amount of time as a single model training run; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve 50% reduction in FLOPs with only 1% drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve 15% reduction in FLOPs without drop in accuracy, while still requiring 3× less training compute than SOTA NAS techniques. Finally, for transfer learning on smaller datasets, our technique identifies 1.2× - 1.4× cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy.

关键词： Training Quantization (signal) Image coding Accuracy Costs Transfer learning computer architecture

来源：评论

学校读者我要写书评

暂无评论

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP f...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sidra Aleem Fangyijie Wang Mayug Maniparambil Eric Arazo Julia Dietlmeier Kathleen Curran Noel E. O’Connor Suzanne Little ML-Labs Dublin City University ML-Labs University College Dublin Centre for Applied AI (CeADAR) University College Dublin Ireland Insight SFI Centre for Data Analytics Dublin City University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt-driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM, to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in-depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part-based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM’s generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training/fine-tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero-shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un-prompted SAM. Code and text prompts are available at SaLIP.

关键词： Image segmentation Adaptation models Filtering Inference mechanisms Lung Robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling

Repeat and Concatenate: 2D to 3D Image Translation with 3D t...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Abril Corona-Figueroa Hubert P. H. Shum Chris G. Willcocks Department of Computer Science Durham University Durham UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space, lose valuable signal information during latent encoding. Instead, we simply repeat and concatenate the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem, sidestepping several complex modeling issues. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin UNETR backbone. Our approach applies neural optimal transport, which is fast and stable to train, effectively integrating signal information across multiple views without the requirement for precise alignment; it produces non-collapsed reconstructions that are highly faithful to the 2D views, even after limited training. We demonstrate correlated results, both qualitatively and quantitatively, having trained our model on a single dataset and evaluated its generalization ability across six datasets, including out-of-distribution samples.

关键词： Training Solid modeling Three-dimensional displays Uncertainty Encoding pattern recognition Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Deploying Machine Learning Anomaly Detection Models to Flight Ready AI Boards

Deploying Machine Learning Anomaly Detection Models to Fligh...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： James Murphy Maria Buckley Léonie Buckley Adam Taylor Jake O’Brien Brian Mac Namee Réaltra Space Systems Engineering Ubotica Technologies Adiuvo Engineering & Training School of Computer Science University College Dublin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study explores the development and implementation of machine learning (ML) models on Edge-AI boards, aiming to identify the most effective solution for anomaly detection systems on space missions. We investigate ML anomaly detection techniques including Autoencoders, Long Short-Term Memory (LSTM) cells, Isolation Forests, and Transformers. These models were trained on a univariate dataset derived from real space missions and deployed on diverse hardware platforms engineered for space environments to comprehensively assess performance. Specifically, we explore space flight ready boards (Ubotica CogniSAT-XE1 and XE2, which incorporate Intel’s Myriad 2 and X chips, respectively); commercial, non-space flight ready, edge-AI boards (NVIDIA’s Jetson Nano and Google Coral); and Field Programmable Gate Array (FPGA) implementations (from Microchip, AMD, and NanoXplore). We compare the performance of anomaly detection models run on space flight ready and commercial boards (using CPU performance as a benchmark) to provide a thorough comparison of available platforms for onboard anomaly detection. This paper provides a detailed examination of both the optimal ML models and hardware platforms for deploying univariate anomaly detection systems in space flight contexts and draws conclusions about which ones are most suitable.

关键词： Performance evaluation Space missions Image edge detection Computational modeling Machine learning Transformers Hardware

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Identification of Arctic Ocean Boundaries and Near-Surface Phenomena in Underwater Echograms

Deep Learning-Based Identification of Arctic Ocean Boundarie...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Femina Senjaliya Melissa Cote Amanda Dash Alexandra Branzan Albu Andrea Niemi Stéphane Gauthier Julek Chawarski Steve Pearce Kaan Ersahin Keath Borg Electrical and Computer Engineering University of Victoria Victoria Canada ASL Environmental Sciences Victoria Canada Fisheries and Oceans Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring marine environments is a crucial part of understanding the impact of oceans on global climate and their importance for biodiversity and ecological systems, particularly in the Arctic region. Underwater active acoustic surveys with moored multi-frequency echosounders allow for the continuous collection of valuable data reflecting the complex dynamics of these environments. This paper addresses the automatic identification of sea surface boundaries and near-surface phenomena in echograms using deep learning methods to support researchers such as biologists in their work, who typically rely on time-consuming manual analyses. We propose a two-step process that first characterizes echograms according to the surface conditions using an image classification paradigm and then identifies the sea surface boundary and near-surface bubbles and their extent in the water column using a semantic segmentation paradigm. Segmentation is carried out using surface type-specific models, which perform better than a single global segmentation model. We also propose learning strategies, such as a custom boundary loss function, that further improve performance. Experiments with various image classification and semantic segmentation architectures allow us to select the most efficient models for Arctic echogram analysis that, when used in conjunction within our proposed pipeline and our learning strategies, offer excellent results.

关键词： Surveys Sea surface Analytical models Biological system modeling Semantic segmentation Arctic Surface treatment

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共332页 << < 219 220 221 222 223 224 225 226 227 228 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：