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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3721-3730 订阅

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Fusing Visual Saliency for Material recognition 31

Fusing Visual Saliency for Material Recognition

引用

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

作者： Qi, Lin Xu, Ying Shang, Xiaowei Dong, Junyu Ocean Univ China 238 Songling Rd Qingdao 266100 Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Material recognition is researched in both computer vision and vision science fields. In this paper, we investigated how humans observe material images and found the eye fixation information improves the performance of material image classification models. We first collected eye-tracking data from human observers and used it to fine-tune a generative adversarial network for saliency prediction (SalGAN). We then fused the predicted saliency map with material images and fed them to CNN models for material classification. The experiment results show that the classification accuracy is improved than those using original images. This indicates that human's visual cues could benefit computational models as priors.

关键词： Computational modeling computer vision Visualization Data models Task analysis Training pattern recognition

来源：评论

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Land Cover Classification from Satellite Imagery With U-Net and Lovasz-Softmax Loss 31

Land Cover Classification from Satellite Imagery With U-Net ...

引用

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

作者： Rakhlin, Alexander Davydow, Alex Nikolenko, Sergey Neuromat OU EE-10111 Tallinn Estonia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The land cover classification task of the DeepGlohe Challenge presents significant obstacles even to state of the art segmentation models due to a small amount of data, incomplete and sometimes incorrect labeling, and highly imbalanced classes. In this work, we show an approach based on the U-Net architecture with the Lovcisz-Softmax loss that successfully alleviates these problems: we compare several different convolutional architectures for U-Net encoders.

关键词： Image segmentation Satellites computer architecture Training Task analysis computer vision Stochastic processes

来源：评论

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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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Peeking into the Future: Predicting Future Person Activities and Locations in Videos

Peeking into the Future: Predicting Future Person Activities...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Junwei Liang Lu Jiang Juan Carlos Niebles Alexander Hauptmann Li Fei-Fei Carnegie Mellon University Google AI Stanford University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Deciphering human behaviors to predict their future paths/trajectories and what they would do from videos is important in many applications. With the advancement in deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos. An important analysis is forecasting the future path of pedestrians, called future person trajectory prediction. This problem has received increasing attention in the computer vision community. It is regarded as an essential building block in video understanding for many applications like self-driving cars, socially-aware robots, etc.

关键词： Videotapes images computer vision PEEK Persons Pedestrians

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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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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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ViDeNN: Deep Blind Video Denoising

ViDeNN: Deep Blind Video Denoising

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Michele Claus Jan van Gemert Computer Vision lab Delft University of Technology

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the noise distribution (blind denoising). The CNN architecture uses a combination of spatial and temporal filtering, learning to spatially denoise the frames first and at the same time how to combine their temporal information, handling objects motion, brightness changes, low-light conditions and temporal inconsistencies. We demonstrate the importance of the data used for CNNs training, creating for this purpose a specific dataset for low-light conditions. We test ViDeNN on common benchmarks and on self-collected data, achieving good results comparable with the state-of-the-art.

关键词： Noise reduction Blind Videotapes Prior Learning Data Collection Methods

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A Holistic Framework for Addressing the World using Machine Learning 31

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

引用

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

引用

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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Notice of Removal: Robust Image Colorization Using Self Attention Based Progressive Generative Adversarial Network

Notice of Removal: Robust Image Colorization Using Self Atte...

引用

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

作者： Manoj Sharma Megh Makwana Avinash Upadhyay Ajay Pratap Singh Anuj Badhwar Akkshita Trivedi Anil Saini Santanu Chaudhury CSIR-CEERI Pilani CCS Computer Pvt Ltd A and N Telecom Circle BSNL DTU Delhi IIT Jodhpur

Removed.

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

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