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

4,653 篇 会议
2 篇 期刊文献

馆藏范围

4,655 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,715 篇 工学
- 1,623 篇 计算机科学与技术...
- 182 篇 软件工程
- 142 篇 机械工程
- 133 篇 光学工程
- 41 篇 生物工程
- 29 篇 信息与通信工程
- 18 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 控制科学与工程
- 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
173 篇 理学
- 135 篇 物理学
- 42 篇 生物学
- 30 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
- 2 篇 大气科学
14 篇 管理学
- 7 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
10 篇 医学
- 10 篇 临床医学
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
3 篇 教育学
- 3 篇 教育学
2 篇 农学
1 篇 经济学

主题

2,867 篇 computer vision
1,227 篇 training
1,038 篇 pattern recognit...
870 篇 computational mo...
794 篇 conferences
693 篇 visualization
593 篇 three-dimensiona...
469 篇 codes
460 篇 benchmark testin...
422 篇 semantics
420 篇 computer archite...
349 篇 accuracy
301 篇 adaptation model...
282 篇 feature extracti...
267 篇 transformers
242 篇 cameras
225 篇 diffusion models
223 篇 solid modeling
214 篇 pipelines
210 篇 measurement

机构

72 篇 tsinghua univers...
69 篇 zhejiang univers...
58 篇 university of sc...
57 篇 shanghai jiao to...
52 篇 google research
47 篇 nanyang technolo...
44 篇 national univers...
40 篇 shanghai ai labo...
39 篇 university of ch...
37 篇 adobe research
37 篇 the university o...
37 篇 the chinese univ...
35 篇 stanford univers...
34 篇 harbin institute...
34 篇 shanghai artific...
34 篇 carnegie mellon ...
33 篇 university of el...
30 篇 peng cheng labor...
30 篇 sun yat-sen univ...
29 篇 s-lab nanyang te...

作者

75 篇 timofte radu
24 篇 yu qiao
22 篇 luc van gool
19 篇 ying shan
16 篇 van gool luc
15 篇 radu timofte
14 篇 xin li
13 篇 li xin
12 篇 chen chen
12 篇 zhang zhao
12 篇 boxin shi
11 篇 lizhuang ma
11 篇 fan haoqiang
11 篇 loy chen change
11 篇 zheng-jun zha
11 篇 liu shuaicheng
11 篇 kai zhang
11 篇 marcos v. conde
11 篇 chen wei-ting
11 篇 ziwei liu

语言

4,654 篇 英文
1 篇 中文

检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

共 4655 条记录，以下是31-40 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：

Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Zhenlin Zhu, Yi Deng, Siqi Mittal, Abhay Chen, Yanbei Wang, Manchen Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Seattle WA 98109 USA Boson AI Santa Clara CA 95054 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350365474

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn't fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： Benchmarking Foundational Models vision and language models Zero-shot recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-perspective Traffic Video Description Model with Fine-grained Refinement Approach

Multi-perspective Traffic Video Description Model with Fine-...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tuan-An To Minh-Nam Tran Trong-Bao Ho Thien-Loc Ha Quang-Tan Nguyen Hoang-Chau Luong Thanh-Duy Cao Minh-Triet Tran Univ Sci VNU HCM Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9798350365474

Analyzing traffic patterns is crucial for enhancing safety and optimizing flow within urban cities. While urban cities possess extensive camera networks for monitoring, the raw video data often lacks the contextual detail necessary for understanding complex traffic incidents and the behaviors of road users. In this paper, we propose a novel methodology for generating comprehensive descriptions of traffic scenarios, combining a vision-language model with rule-based refinements to capture pertinently pedestrian, vehicle, and environment factors. First, a captioning model will generate a general description using processed video as input. Subsequently, this description is refined sequentially through three primary modules: pedestrian-aware, vehicle-aware, and context-aware, enhancing the final description. We evaluate our method on the Woven Traffic Safety datasets in Track 2 of the AI City Challenge 2024, obtaining competitive results with an S2 score of 22.6721. Code will be available at https://***/ToTuanAn/AICityChallenge2024_Track2

关键词： AI City Challenge 2024 fine-grained refinement multi-perspective

来源：评论

学校读者我要写书评

暂无评论

Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kim, Taehoon Marsden, Mark Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Sala, Alessandra Kim, Seung Hwan LG AI Res Seoul South Korea Shutterstock New York NY USA

ISBN: (纸本)9798350365474

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： image captioning large-scale multimodal transformers vision-language zero-shot

来源：评论

学校读者我要写书评

暂无评论

HyperLeaf2024-A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024-A Hyperspectral Imaging Dataset for Classifica...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Laprade, William Michael Pieta, Pawel Kutuzova, Svetlana Westergaard, Jesper Cairo Nielsen, Mads Christensen, Svend Dahl, Anders Bjorholm Tech Univ Denmark Dept Appl Math & Comp Sci Copenhagen Denmark Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Dept Plant & Environm Sci Copenhagen Denmark

ISBN: (纸本)9798350365474

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellitebased land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on perpixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in wholeimage hyperspectral image analysis, we introduce the HyperLeaf2024 dataset. The dataset consists of 2410 hyperspectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyperspectral image analysis.

关键词： dataset deep learning hyperspectral imaging plant science

来源：评论

学校读者我要写书评

暂无评论

Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shoman, Maged Wang, Dongdong Aboah, Armstrong Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL 32816 USA North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA Univ Cent Florida Joint Appointment Dept Comp Sci Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL USA

ISBN: (纸本)9798350365474

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The opensource code will be available at https://***/UCF-SST-Lab/AICity2024cvprw

关键词： computer vision cross-modality learning dense video captioning domain adaptation natural language processing

来源：评论

学校读者我要写书评

暂无评论

Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sharif, S. M. A. Myrzabekov, Azamat Khujaev, Nodirkhuja Tsoy, Roman Kim, Seongwan Lee, Jaeho LG Sciencepk Seoul South Korea

ISBN: (纸本)9798350365474

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Edge Device Edge LLIE Edge vision Task Low-light Image Enhancement Optimized LLIE Quantization

来源：评论

学校读者我要写书评

暂无评论

Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cieslak, Mikolaj Govindarajan, Umabharathi Garcia, Alejandro Chandrashekar, Anuradha Haedrich, Torsten Mendoza-Drosik, Aleksander Michels, Dominik L. Pirk, Soeren Fu, Chia-Chun Palubicki, Wojciech GreenMatterAI Berlin Germany Blue River Technol Santa Clara CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Tech Univ Darmstadt Darmstadt Germany Christian Albrecht Univ Kiel Kiel Germany Adam Mickiewicz Univ Poznan Poland

ISBN: (纸本)9798350365474

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Low Latency Point Cloud Rendering with Learned Splatting

Low Latency Point Cloud Rendering with Learned Splatting

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hu, Yueyu Gong, Ran Sun, Qi Wang, Yao NYU Tandon Sch Engn Brooklyn NY 11201 USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350365474

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts.

关键词： Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling

Label-free Anomaly Detection in Aerial Agricultural Images w...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shikhar, Sambal Sobti, Anupam Plaksha Univ Mohali Punjab India

ISBN: (纸本)9798350365474

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only "normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating "normal" images for training. Evaluation on the Agriculture-vision data challenge shows a 6.3% mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-vision Challenge Dataset [5].

关键词： Anomaly Detection computer vision Masked Image Modelling Precision Agriculture UAV Images

来源：评论

学校读者我要写书评

暂无评论

Towards Efficient Audio-Visual Learners via Empowering Pre-trained vision Transformers with Cross-Modal Adaptation

Towards Efficient Audio-Visual Learners via Empowering Pre-t...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Kai Tian, Yapeng Hatzinakos, Dimitrios Univ Toronto Toronto ON Canada Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9798350365474

In this paper, we explore the cross-modal adaptation of pre-trained vision Transformers (ViTs) for the audio-visual domain by incorporating a limited set of trainable parameters. To this end, we propose a Spatial-Temporal-Global Cross-Modal Adaptation (STG-CMA) to gradually equip the frozen ViTs with the capability for learning audio-visual representation, consisting of the modality-specific temporal adaptation for temporal reasoning of each modality, the cross-modal spatial adaptation for refining the spatial information with the cue from counterpart modality, and the cross-modal global adaptation for global interaction between audio and visual modalities. Our STG-CMA presents a meaningful finding that only leveraging the shared pre-trained image model with inserted lightweight adapters is enough for spatial-temporal modeling and feature interaction of audio-visual modality. Extensive experiments indicate that our STG-CMA achieves state-of-the-art performance on various audio-visual understanding tasks including AVE, AVS, and AVQA while containing significantly reduced tunable parameters. The code is available at https://***/kaiw7/STG-CMA.

关键词： Audio-visual Lenarning Cross-modal Adaptation Pre-trained vision Transformers Reduced Tunanle Parameters Spatial-temporal-global Modeling

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466页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：