检索结果-内蒙古大学图书馆

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Stieler, Fabian Rabe, Fabian Bauer, Bernhard Univ Augsburg Augsburg Germany

ISBN: (纸本)9781665448994

Machine Learning models have started to outperform medical experts in some classification tasks. Meanwhile, the question of how these classifiers produce certain results is attracting increasing research attention. Current interpretation methods provide a good starting point in investigating such questions, but they still massively lack the relation to the problem domain. In this work, we present how explanations of an AI system for skin image analysis can be made more domain-specific. We apply the synthesis of Local Interpretable Model-agnostic Explanations (LIME) with the ABCD-rule, a diagnostic approach of dermatologists, and present the results using a Deep Neural Network (DNN) based skin image classifier.

关键词： Deep learning computer vision Image analysis Computational modeling conferences Skin pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Robust and Online Vehicle Counting at Crowded Intersections

Robust and Online Vehicle Counting at Crowded Intersections

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lu, Jincheng Xia, Meng Gao, Xu Yang, Xipeng Tao, Tianran Meng, Hao Zhang, Wei Tan, Xiao Shi, Yifeng Li, Guanbin Ding, Errui

ISBN: (纸本)9781665448994

In this paper, we propose an online movement-specific vehicle counting system to realize robust traffic flow analysis at crowded intersections. Our proposed framework adopts PP-YOLO as the vehicle detector and adapts the Deep-Sort algorithm to perform multi-object tracking. In order to realize online and robust vehicle counting, we further adopt a shape-based movement assignment strategy to differentiate movements and carefully designed spatial constraints to effectively reduce false-positive counts. Our proposed framework achieves the overall S1-score of 0.9467, ranking the first in the AICITY2021-track1 challenge.

关键词： computer vision Tracking conferences Detectors pattern recognition

来源：评论

学校读者我要写书评

暂无评论

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Clas...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Conde, Marcos, V Turgutlu, Kerem Univ Valladolid Valladolid Spain Adobe Mountain View CA USA

ISBN: (纸本)9781665448994

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. In this work, we use CLIP (Contrastive Language-Image Pre-Training) [12] for training a neural network on a variety of art images and text pairs, being able to learn directly from raw descriptions about images, or if available, curated labels. Model's zero-shot capability allows predicting the most relevant natural language description for a given image, without directly optimizing for the task. Our approach aims to solve 2 challenges: instance retrieval and fine-grained artwork attribute recognition. We use the iMet Dataset [20], which we consider the largest annotated artwork dataset. Our code and models will be available at https://***/KeremTurgutlu/clip_art

关键词： Training computer vision Art Image recognition Databases Natural languages Neural networks

来源：评论

学校读者我要写书评

暂无评论

Scalable and Explainable Outfit Generation

Scalable and Explainable Outfit Generation

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lorbert, Alexander Neiman, David Poznanski, Arik Oks, Eduard Davis, Larry Amazon Seattle WA 98109 USA

ISBN: (纸本)9781665448994

We present an end-to-end system for learning outfit recommendations. The core problem we address is how a customer can receive clothing/accessory recommendations based on a current outfit and what type of item the customer wishes to add to the outfit. Using a repository of coherent and stylish outfits, we leverage self-attention to learn a mapping from the current outfit and the customer-requested category to a visual descriptor output. This output is then fed into nearest-neighbor-based visual search, which, during training, is learned via triplet loss and mini-batch retrievals. At inference time, we use a beam search with a desired outfit composition to generate outfits at scale. Moreover, the attention networks provide a diagnostic look into the recommendation process, serving as a fashion-based sanity check.

关键词： Training Visualization computer vision conferences pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Shadow Removal with Paired and Unpaired Learning

Shadow Removal with Paired and Unpaired Learning

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vasluianu, Florin-Alexandru Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photorealistic restoration of the image contents. Decades of research produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shadowed and shadow free training image pairs. In this work, we propose a single image shadow removal solution via self-supervised learning by using a conditioned mask. We rely on self-supervision and jointly learn deep models to remove and add shadows to images. We derive two variants for learning from paired images and unpaired images, respectively. Our validation on the recently introduced ISTD and USR datasets demonstrate large quantitative and qualitative improvements over the state-of-the-art for both paired and unpaired learning settings.

关键词： Training computer vision conferences Image restoration pattern recognition Task analysis Light sources

来源：评论

学校读者我要写书评

暂无评论

Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution

Pixel-Guided Dual-Branch Attention Network for Joint Image D...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xi, Si Wei, Jia Zhang, Weidong Netease Games AI Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.

关键词： Training computer vision conferences Superresolution Feature extraction Image restoration pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Fourier Feature-based CBAM and vision Transformer for Text Detection in Drone Images 17th

Fourier Feature-based CBAM and Vision Transformer for Text D...

引用

17th International conference on Document Analysis and recognition Workshop (ICDAR)

作者： Roy, Ayush Shivakumara, Palaiahnakote Pal, Umapada Mokayed, Hamam Liwicki, Marcus Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata India Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Lulea Univ Technol Dept Comp Sci Elect & Space Engn Lulea Sweden

ISBN: (纸本)9783031415005;9783031415012

The use of drones for several real-world applications is increasing exponentially, especially for the purpose of monitoring, surveillance, security, etc. Most existing scene text detection methods were developed for normal scene images. This work aims to develop a model for detecting text in drone as well as scene images. To reduce the adverse effects of drone images, we explore the combination of Fourier transform and Convolutional Block Attention Module (CBAM) to enhance the degraded information in the images without affecting high-contrast images. This is because the above combination helps us to extract prominent features which represent text irrespective of degradations. Therefore, the refined features extracted from the Fourier Contouring Network (FCN) are supplied to vision Transformer, which uses the ResNet50 as a backbone and encoder-decoder for text detection in both drone and scene images. Hence, the model is called Fourier Transform based Transformer. Experimental results on drone datasets and benchmark datasets, namely, Total-Text and ICDAR 2015 of natural scene text detection show the proposed model is effective and outperforms the state-of-the-art models.

关键词： Scene text detection Drone images Deep learning Transformer Detection transformer

来源：评论

学校读者我要写书评

暂无评论

ObjectGraphs: Using Objects and a Graph Convolutional Network for the Bottom-up recognition and Explanation of Events in Video

ObjectGraphs: Using Objects and a Graph Convolutional Networ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gkalelis, Nikolaos Goulas, Andreas Galanopoulos, Damianos Mezaris, Vasileios CERTH ITI 6th Km Charilaou Thermi RdPOB 60361 Thessaloniki Greece

ISBN: (纸本)9781665448994

In this paper a novel bottom-up video event recognition approach is proposed, ObjectGraphs, which utilizes a rich frame representation and the relations between objects within each frame. Following the application of an object detector (OD) on the frames, graphs are used to model the object relations and a graph convolutional network (GCN) is utilized to perform reasoning on the graphs. The resulting object-based frame-level features are then forwarded to a long short-term memory (LSTM) network for video event recognition. Moreover, the weighted in-degrees (WiDs) derived from the graph's adjacency matrix at frame level are used for identifying the objects that were considered most (or least) salient for event recognition and contributed the most (or least) to the final event recognition decision, thus providing an explanation for the latter. The experimental results show that the proposed method achieves state-of-the-art performance on the publicly available FCVID and YLI-MED datasets(1).

关键词： computer vision Computational modeling conferences Detectors Cognition pattern recognition Object recognition

来源：评论

学校读者我要写书评

暂无评论

Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images

Training Rare Object Detection in Satellite Imagery with Syn...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Martinson, Eric Furlong, Bridget Gillies, Andy Soar Technol 3600 Green StSte 600 Ann Arbor MI 48105 USA

ISBN: (纸本)9781665448994

When creating a new labeled dataset, human analysts or data reductionists must review and annotate large numbers of images. This process is time consuming and a barrier to the deployment of new computer vision solutions, particularly for rarely occurring objects. To reduce the number of images requiring human attention, we evaluate the utility of images created from 3D models refined with a generative adversarial network to select confidence thresholds that significantly reduce false alarms rates. The resulting approach has been demonstrated to cut the number of images needing to be reviewed by 50% while preserving a 95% recall rate, with only 6 labeled examples of the target.

关键词： Training Solid modeling computer vision Analytical models Three-dimensional displays Satellites Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture recognition

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3422-3433页

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA JD Finance Mountain View CA 94043 USA Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Xian Key Lab Big Data & Intelligent Vis Xian 710071 Peoples R China Univ Barcelona Comp Vis Ctr Barcelona 08007 Spain Univ Tartu Inst Technol iCV Lab EE-50090 Tartu Estonia PwC Finland Helsinki 00180 Finland Hasan Kalyoncu Univ Fac Engn TR-27100 Gaziantep Turkey ChaLearn San Francisco CA 94115 USA Univ Paris Saclay F-91190 St Aubin France Baidu Res Inst Deep Learning Beijing 100193 Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing 100193 Peoples R China Westlake Univ Hangzhou 310024 Peoples R China Macau Univ Sci & Technol Taipa Macau Peoples R China

The ChaLearn large-scale gesture recognition challenge has run twice in two workshops in conjunction with the International conference on pattern recognition (ICPR) 2016 and International conference on computer vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.

关键词： Gesture recognition Measurement Task analysis Training conferences computer vision Bidirectional long short-term memory (Bi-LSTM) gesture recognition RGB-D

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：