Zhuowen Tu spends much of his time researching Artificial intelligence, Pattern recognition, Image segmentation, Machine learning and Convolutional neural network. As part of his studies on Artificial intelligence, Zhuowen Tu frequently links adjacent subjects like Computer vision. His work on Segmentation as part of general Pattern recognition research is frequently linked to Boundary detection, thereby connecting diverse disciplines of science.
His research integrates issues of Markov chain and Cluster analysis in his study of Image segmentation. His Convolutional neural network study combines topics in areas such as Artificial neural network, Enhanced Data Rates for GSM Evolution, Feature learning and Feature. His Contextual image classification research is multidisciplinary, incorporating perspectives in Dimension, Theoretical computer science and Robustness.
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Discriminative model. His studies in Image segmentation, Convolutional neural network, Segmentation, Image and Boosting are all subfields of Artificial intelligence research. His Pattern recognition study also includes fields such as
While the research belongs to areas of Machine learning, Zhuowen Tu spends his time largely on the problem of Benchmark, intersecting his research to questions surrounding Data mining and Function. His Computer vision research includes themes of Pattern recognition and Medical imaging. As part of the same scientific family, Zhuowen Tu usually focuses on Discriminative model, concentrating on Generative model and intersecting with Matching.
Zhuowen Tu focuses on Artificial intelligence, Convolutional neural network, Pattern recognition, Artificial neural network and Machine learning. His work in Representation, Feature learning, Convolution, Image and Discriminative model are all subfields of Artificial intelligence research. His Convolutional neural network research incorporates themes from Backpropagation, Enhanced Data Rates for GSM Evolution, Benchmark, MNIST database and Feature extraction.
His Pattern recognition study incorporates themes from Pixel and Transformer. The Artificial neural network study combines topics in areas such as Data mining, Contextual image classification, Boosting, Unsupervised learning and Robustness. He interconnects Classifier and Pooling in the investigation of issues within Machine learning.
Zhuowen Tu mainly investigates Artificial intelligence, Convolutional neural network, Feature learning, Feature and Pattern recognition. Artificial neural network, Classifier, MNIST database, Discriminative model and Backpropagation are the primary areas of interest in his Artificial intelligence study. As a part of the same scientific family, Zhuowen Tu mostly works in the field of Artificial neural network, focusing on Data mining and, on occasion, Dimension.
His study in Convolutional neural network is interdisciplinary in nature, drawing from both Enhanced Data Rates for GSM Evolution, Segmentation, Representation and Benchmark. His studies in Enhanced Data Rates for GSM Evolution integrate themes in fields like Object detection, Feature extraction, Image segmentation and Edge detection. His studies deal with areas such as Convolution and FLOPS as well as Feature.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie;Ross Girshick;Piotr Dollar;Zhuowen Tu.
computer vision and pattern recognition (2017)
Holistically-Nested Edge Detection
Saining Xie;Zhuowen Tu.
international conference on computer vision (2015)
Deeply-Supervised Nets
Chen-Yu Lee;Saining Xie;Patrick W. Gallagher;Zhengyou Zhang.
international conference on artificial intelligence and statistics (2015)
Integral Channel Features
Piotr Dollár;Zhuowen Tu;Pietro Perona;Serge J. Belongie.
british machine vision conference (2009)
Similarity network fusion for aggregating data types on a genomic scale
Bo Wang;Aziz M Mezlini;Feyyaz Demir;Marc Fiume.
Nature Methods (2014)
Deeply Supervised Salient Object Detection with Short Connections
Qibin Hou;Ming-Ming Cheng;Xiaowei Hu;Ali Borji.
computer vision and pattern recognition (2017)
Deeply Supervised Salient Object Detection with Short Connections
Qibin Hou;Ming-Ming Cheng;Xiaowei Hu;Ali Borji.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)
Image segmentation by data-driven Markov chain Monte Carlo
Zhuowen Tu;Song-Chun Zhu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Image parsing: unifying segmentation, detection, and recognition
Zhuowen Tu;Xiangrong Chen;Yuille;Zhu.
international conference on computer vision (2003)
Image Parsing: Unifying Segmentation, Detection, and Recognition
Zhuowen Tu;Xiangrong Chen;Alan L. Yuille;Song-Chun Zhu.
International Journal of Computer Vision (2005)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Huazhong University of Science and Technology
University of Southern California
Johns Hopkins University
University of Southern California
Sichuan University
Huazhong University of Science and Technology
Microsoft (United States)
Huazhong University of Science and Technology
Peking University
Medical University of Vienna
Changsha University of Science and Technology
Osaka University
Max Planck Society
University of California, San Francisco
Max Planck Society
University of California, Davis
University of Manchester
Ludwig-Maximilians-Universität München
Goddard Space Flight Center
University of Campania "Luigi Vanvitelli"
Northeastern University
Wichita State University
University of Manchester
University of Pittsburgh
University of Edinburgh
University of Pennsylvania