Yan Yan spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Multi-task learning. His study in Feature, Convolutional neural network, Training set, Support vector machine and Face is done as part of Artificial intelligence. In his study, which falls under the umbrella issue of Feature, Noise reduction, Motion, Contrast and Matching is strongly linked to Representation.
His study looks at the relationship between Pattern recognition and topics such as Deep learning, which overlap with Anomaly detection. His research investigates the link between Machine learning and topics such as Visualization that cross with problems in Semi-supervised learning. His study looks at the intersection of Discriminative model and topics like Feature extraction with Landmark.
Yan Yan mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Discriminative model. Artificial intelligence is represented through his Feature, Deep learning, Image, Pose and Face research. His Feature selection and Classifier study in the realm of Pattern recognition connects with subjects such as Invariant and Constraint.
Yan Yan regularly ties together related areas like Contextual image classification in his Machine learning studies. When carried out as part of a general Computer vision research project, his work on Optical flow and Gaze is frequently linked to work in GPS signals, therefore connecting diverse disciplines of study. His studies deal with areas such as Object and Feature extraction as well as Discriminative model.
His primary scientific interests are in Artificial intelligence, Modal, Applied mathematics, Pattern recognition and Computer vision. His Artificial intelligence study frequently links to related topics such as Machine learning. His research integrates issues of Object and Conditional random field in his study of Machine learning.
His biological study deals with issues like Smooth pursuit, which deal with fields such as Statistical algorithm and Robustness. His work in the fields of Computer vision, such as Enhanced Data Rates for GSM Evolution, Shadow and Image, overlaps with other areas such as Distortion and Market segmentation. Task and Semantics is closely connected to Discriminative model in his research, which is encompassed under the umbrella topic of Feature.
Yan Yan mostly deals with Artificial intelligence, Applied mathematics, Duality gap, Convexity and Feature. His Artificial intelligence research integrates issues from Machine learning and Computer vision. His work on Margin as part of general Machine learning research is frequently linked to Rank, bridging the gap between disciplines.
Yan Yan combines subjects such as Gradient descent and Stochastic gradient descent with his study of Applied mathematics. Yan Yan has included themes like Image generation and Generative adversarial network in his Feature study. The various areas that Yan Yan examines in his Artificial neural network study include Contrast, Computation and Canonical correlation.
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Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
Dan Xu;Elisa Ricci;Yan Yan;Jingkuan Song.
british machine vision conference (2015)
Detecting anomalous events in videos by learning deep representations of appearance and motion
Dan Xu;Yan Yan;Elisa Ricci;Elisa Ricci;Nicu Sebe.
Computer Vision and Image Understanding (2017)
A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification
Yutian Lin;Xuanyi Dong;Liang Zheng;Yan Yan.
national conference on artificial intelligence (2019)
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning
Yu Wu;Yutian Lin;Xuanyi Dong;Yan Yan.
computer vision and pattern recognition (2018)
Fortune teller: predicting your career path
Ye Liu;Luming Zhang;Liqiang Nie;Yan Yan.
national conference on artificial intelligence (2016)
Person Re-identification via Recurrent Feature Aggregation
Yichao Yan;Bingbing Ni;Zhichao Song;Chao Ma.
european conference on computer vision (2016)
Multitask Linear Discriminant Analysis for View Invariant Action Recognition
Yan Yan;Elisa Ricci;Ramanathan Subramanian;Gaowen Liu.
IEEE Transactions on Image Processing (2014)
Style Aggregated Network for Facial Landmark Detection
Xuanyi Dong;Yan Yan;Wanli Ouyang;Yi Yang.
computer vision and pattern recognition (2018)
Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition
Yahong Han;Yi Yang;Yan Yan;Zhigang Ma.
IEEE Transactions on Neural Networks (2015)
Recurrent Face Aging
Wei Wang;Zhen Cui;Yan Yan;Jiashi Feng.
computer vision and pattern recognition (2016)
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