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2008, Di 1 ban., ISBN 9787807521006, 91
Book
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 03/2009, Volume 31, Issue 3, pp. 539 - 555
Journal Article
2013 IEEE International Conference on Computer Vision, ISSN 1550-5499, 12/2013, pp. 2056 - 2063
Conference Proceeding
2009, Social transformations in Chinese societies, ISBN 900418192X, Volume 5, xiv, 268
Book
2013 IEEE International Conference on Computer Vision, ISSN 1550-5499, 12/2013, pp. 2528 - 2535
Human salience is distinctive and reliable information in matching pedestrians across disjoint camera views. In this paper, we exploit the pair wise salience... 
Measurement | Visualization | patch matching | salience matching | Vectors | saliency | Person re-identification | Training | Support vector machines | salience | Image color analysis | Cameras | pedestrian matching | Learning | Human | Matching | Computer vision | Pedestrians | Conferences | Images | Strategy | Cost engineering
Conference Proceeding
亚洲农业研究:英文版, ISSN 1943-9903, 2013, Volume 5, Issue 5, pp. 50 - 51
Through the overview of main problems existing in chives production, its current situation and history, the thesis puts forward countermeasures for developing... 
Countermeasur | industry | Present | situation | of | Chives
Journal Article
Optics Letters, ISSN 0146-9592, 09/2013, Volume 38, Issue 18, pp. 3684 - 3686
Journal Article
2013 IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 06/2013, pp. 3594 - 3601
In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person... 
Measurement | Training | Learning systems | Visualization | Transforms | Cameras | Vectors
Conference Proceeding
2014 IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 06/2014, pp. 1891 - 1898
This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face... 
Training | deep learning | Neurons | Feature extraction | face verification | Bayes methods | Face | Joints | Biological neural networks | Learning | Classifiers | Computer vision | Tasks | Activation | Pattern recognition | Representations
Conference Proceeding
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 06/2016, Volume 2016-, pp. 1249 - 1258
Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that... 
Training | Neurons | Pipelines | Machine learning | Feature extraction | Robustness | Biological neural networks
Conference Proceeding
2015 IEEE International Conference on Computer Vision (ICCV), ISSN 1550-5499, 12/2015, Volume 2015, pp. 3119 - 3127
We propose a new approach for general object tracking with fully convolutional neural network. Instead of treating convolutional neural network (CNN) as a... 
Visualization | Target tracking | Semantics | Neurons | Feature extraction | Robustness | Noise measurement | Computer vision | Tracking | Neural networks | Redundancy | Categories | Detectors
Conference Proceeding
Pattern Recognition Letters, ISSN 0167-8655, 01/2013, Volume 34, Issue 1, pp. 3 - 19
Journal Article
2013 IEEE International Conference on Computer Vision, ISSN 1550-5499, 12/2013, pp. 113 - 120
Face recognition with large pose and illumination variations is a challenging problem in computer vision. This paper addresses this challenge by proposing a... 
Training | deep learning | Three-dimensional displays | Face recognition | Lighting | Feature extraction | Face | Image reconstruction | face recognition | Learning | Computer vision | Databases | Conferences | Images | Illumination | Representations
Conference Proceeding
2013 IEEE International Conference on Computer Vision, ISSN 1550-5499, 12/2013, pp. 1489 - 1496
This paper proposes a hybrid convolutional network (ConvNet)-Restricted Boltzmann Machine (RBM) model for face verification in wild conditions. A key... 
Training | Visualization | deep learning | Computational modeling | Face recognition | Neurons | relational visual features | Feature extraction | face verification | Face | Learning | Computer vision | Networks | Analogies | Hierarchies | Tasks | Conferences | Robustness | Visual
Conference Proceeding
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 06/2015, Volume 7-12-, pp. 2892 - 2900
This paper designs a high-performance deep convolutional network (DeepID2+) for face recognition. It is learned with the identification-verification... 
Training | Accuracy | Convolution | Face recognition | Neurons | Robustness | Face | Occlusion | Computer vision | Activation | Pattern recognition | Representations | Activation analysis
Conference Proceeding
05/2013
Through the overview of main problems existing in chives production, its current situation and history, the thesis puts forward countermeasures for developing... 
Countermeasures for development | Agribusiness | Present situation of industry | Xiaogang Town | Chives
Journal Article
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 06/2015, Volume 7-12-, pp. 1265 - 1274
Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast... 
Context | Training | Visualization | Object detection | Machine learning | Predictive models | Context modeling | Learning | Computer vision | Conferences | Neural networks | Strategy | Pattern recognition | Visual | Image classification
Conference Proceeding
2014 IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 06/2014, pp. 152 - 159
Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detectors. Challenges are presented in the form of... 
Training | Image color analysis | Neural networks | Person Re-Identification | Transforms | Cameras | Feature extraction | Clutter | Pedestrians | Misalignment | Detectors | Photometry | Image detection
Conference Proceeding
2013 IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 06/2013, pp. 3586 - 3593
Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing... 
Support vector machines | Training | Histograms | Image color analysis | Salience matching | recognition | Cameras | Feature extraction | Vectors | person re-identification
Conference Proceeding
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