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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Comparison of deep transfer learning strategies for digital pathology 31

Comparison of deep transfer learning strategies for digital ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mormont, Romain Geurts, Pierre Maree, Raphael Univ Liege Liege Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we study deep transfer learning as a way of overcoming object recognition challenges encountered in the field of digital pathology. Through several experiments, we investigate various uses of pre-trained neural network architectures and different combination schemes with random forests for feature selection. Our experiments on eight classification datasets show that densely connected and residual networks consistently yield best performances across strategies. It also appears that network fine-tuning and using inner layers features are the best performing strategies, with the former yielding slightly superior results.

关键词： Feature extraction Task analysis Biomedical imaging Training Support vector machines computer vision

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Word Spotting in Scene Images based on Character recognition 31

Word Spotting in Scene Images based on Character Recognition

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bazazian, Dena Karatzas, Dimosthenis Bagdanov, Andrew D. Univ Autonoma Barcelona CVC Barcelona Spain Univ Florence MICC I-50121 Florence Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.

关键词： Proposals Histograms Character recognition Text recognition Detectors Heating systems Image recognition

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Rotated Rectangles for Symbolized Building Footprint Extraction 31

Rotated Rectangles for Symbolized Building Footprint Extract...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dickenson, Matt Gueguen, Lionel UBER Technol Inc San Francisco CA 94103 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Building footprints (BFP) provide useful visual context for users of digital maps when navigating in space. This paper proposes a method for extracting and symbolizing building footprints from satellite imagery using a convolutional neural network (CNN). The CNN architecture outputs rotated rectangles, providing a symbolized approximation that works well for small buildings. Experiments are conducted on the four cities in the DeepGlobe Challenge dataset (Las Vegas, Paris, Shanghai, Khartoum). Our method performs best on suburbs consisting of individual houses. These experiments show that either large buildings or buildings without clear delineation produce weaker results in terms of precision and recall.

关键词： Buildings computer architecture Microprocessors Architecture Urban areas computer vision Satellites

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Human Action Adverb recognition: ADHA Dataset and A Three-Stream Hybrid Model 31

Human Action Adverb Recognition: ADHA Dataset and A Three-St...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pang, Bo Zha, Kaiwen Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We introduce the first benchmark for a new problem - recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). We demonstrate some key features of ADHA: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labelling of simultaneously emerging actions in each video. We commit an in-depth analysis on the implementation of current effective models in action recognition and image captioning on adverb recognition, and the results reveal that such methods are unsatisfactory. Furthermore, we propose a novel three-stream hybrid model to tackle the HAA problem, which achieves better performances and receives relatively promising results.

关键词： Image recognition Mood Emotion recognition Sports Benchmark testing Face pattern recognition

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Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery 31

Stacked U-Nets for Ground Material Segmentation in Remote Se...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ghosh, Arthita Ehrlich, Max Shah, Sohil Davis, Larry Chellappa, Rama Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.

关键词： Semantics Image segmentation Remote sensing Convolution Training Satellites computer architecture

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NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation 31

NU-Net: Deep Residual Wide Field of View Convolutional Neura...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Samy, Mohamed Amer, Karim Eissa, Kareem Shaker, Mahmoud ElHelw, Mohamed Nile Univ Ctr Informat Sci Giza Egypt

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the input spatial size and a decoder that transforms the encoder's feature map into a predicted mask with the original size. We change this conventional architecture to a model that makes use of full resolution information. NU-Net is a deep FCNN that is able to capture wide field of view global information around each pixel while maintaining localized full resolution information throughout the model. We evaluate our model on the Land Cover Classification and Road Extraction tracks in the DeepGlobe competition.

关键词： Image segmentation Semantics Roads computer architecture Convolution computer vision pattern recognition

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Deep Convolutional Neural Networks with Integrated Quadratic Correlation Filters for Automatic Target recognition 31

Deep Convolutional Neural Networks with Integrated Quadratic...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Millikan, Brian Foroosh, Hassan Sun, Qiyu Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA Univ Cent Florida Dept Math Orlando FL 32816 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Automatic target recognition involves detecting and recognizing potential targets automatically, which is widely used in civilian and military applications today. Quadratic correlation filters were introduced as two-class recognition classifiers for quickly detecting targets in cluttered scene environments. In this paper, we introduce two methods that integrate the discrimination capability of quadratic correlation filters with the multi-class recognition ability of multilayer neural networks. For mid-wave infrared imagery, the proposed methods are demonstrated to be multi-class target recognition classifiers with very high accuracy.

关键词： Target recognition Correlation Training Object detection Standards Convolutional neural networks

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I Know How You Feel: Emotion recognition with Facial Landmarks 31

I Know How You Feel: Emotion Recognition with Facial Landmar...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tautkute, Ivona Trzcinski, Tomasz Bielski, Adam Tooploox Wroclaw Poland Polish Japanese Acad Informat Technol Warsaw Poland Warsaw Univ Technol Warsaw Poland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multilayered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN), that achieves state-of-the-art results in the recent facial landmark recognition challenge, with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%.

关键词： Emotion recognition Task analysis Face Face recognition Convolutional neural networks Databases

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A Holistic Framework for Addressing the World using Machine Learning 31

A Holistic Framework for Addressing the World using Machine ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Raskar, Ramesh Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Facebook Menlo Pk CA 94025 USA MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase.

关键词： Roads Urban areas Satellites Image segmentation computer vision conferences Topology

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Cross-domain fashion image retrieval 31

Cross-domain fashion image retrieval

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gajic, Bojana Baldrich, Ramon Univ Autonoma Barcelona Comp Vis Ctr Edifici O UAB Bellaterra Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task.

关键词： Task analysis Streaming media computer vision Image retrieval Biological system modeling conferences Training

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