H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 77 Citations 23,151 242 World Ranking 543 National Ranking 44

Research.com Recognitions

Awards & Achievements

2020 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to shape representation, contextual visual similarity, and camera-based document analysis

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Artificial neural network. His study focuses on the intersection of Artificial intelligence and fields such as Machine learning with connections in the field of Representation. His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification and Metric.

The various areas that he examines in his Computer vision study include Character, Algorithm design, Word and Robustness. As a part of the same scientific family, Xiang Bai mostly works in the field of Feature extraction, focusing on Margin and, on occasion, Slope field, Time complexity and Pairwise comparison. His study in Artificial neural network is interdisciplinary in nature, drawing from both Text mining, Speech recognition and Spotting.

His most cited work include:

  • An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition (966 citations)
  • Detecting texts of arbitrary orientations in natural images (537 citations)
  • AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (519 citations)

What are the main themes of his work throughout his whole career to date?

Xiang Bai mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Object detection and Segmentation. His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Pascal. His research investigates the connection between Pattern recognition and topics such as Feature that intersect with issues in Feature vector.

Computer vision is represented through his Pixel, Object and Topological skeleton research. His research in Topological skeleton intersects with topics in Algorithm and Skeleton. His work carried out in the field of Object detection brings together such families of science as Cognitive neuroscience of visual object recognition, Minimum bounding box and Robustness.

He most often published in these fields:

  • Artificial intelligence (88.69%)
  • Pattern recognition (56.57%)
  • Computer vision (28.13%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (88.69%)
  • Pattern recognition (56.57%)
  • Segmentation (14.98%)

In recent papers he was focusing on the following fields of study:

Xiang Bai focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Deep learning. Xiang Bai interconnects Generator and Machine learning in the investigation of issues within Artificial intelligence. His Feature extraction study in the realm of Pattern recognition interacts with subjects such as Set.

Xiang Bai combines subjects such as Discriminative model, Sensor array and Data pre-processing with his study of Feature extraction. The concepts of his Segmentation study are interwoven with issues in Representation, Pascal and Spotting. His study in the fields of Text detection, Object and Crowd counting under the domain of Computer vision overlaps with other disciplines such as Scale and Conjunction.

Between 2019 and 2021, his most popular works were:

  • PCL: Proposal Cluster Learning for Weakly Supervised Object Detection (116 citations)
  • Deep-Person: Learning discriminative deep features for person Re-Identification (73 citations)
  • Predicting COVID-19 malignant progression with AI techniques (48 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Xiang Bai mainly focuses on Artificial intelligence, Pattern recognition, Object detection, Segmentation and Semantics. Xiang Bai has researched Artificial intelligence in several fields, including Machine learning and Computer vision. His Computer vision study incorporates themes from Vertex and Training set.

Xiang Bai is interested in Convolutional neural network, which is a branch of Pattern recognition. As a member of one scientific family, Xiang Bai mostly works in the field of Object detection, focusing on Point cloud and, on occasion, Frame rate, Robustness and Data mining. His research integrates issues of Representation, Spotting and Benchmark in his study of Segmentation.

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.

Top Publications

An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition

Baoguang Shi;Xiang Bai;Cong Yao.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)

1157 Citations

AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Gui-Song Xia;Jingwen Hu;Fan Hu;Baoguang Shi.
IEEE Transactions on Geoscience and Remote Sensing (2017)

644 Citations

Detecting texts of arbitrary orientations in natural images

Cong Yao;Xiang Bai;Wenyu Liu;Yi Ma.
computer vision and pattern recognition (2012)

620 Citations

Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

Zhuowen Tu;Xiang Bai.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)

605 Citations

Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution

Xiang Bai;L.J. Latecki;Wen-Yu Liu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

545 Citations

Path Similarity Skeleton Graph Matching

Xiang Bai;L.J. Latecki.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)

485 Citations

Richer Convolutional Features for Edge Detection

Yun Liu;Ming-Ming Cheng;Xiaowei Hu;Jia-Wang Bian.
computer vision and pattern recognition (2017)

474 Citations

AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Gui-Song Xia;Jingwen Hu;Fan Hu;Baoguang Shi.
IEEE Transactions on Geoscience and Remote Sensing (2017)

462 Citations

TextBoxes: a fast text detector with a single deep neural network

Minghui Liao;Baoguang Shi;Xiang Bai;Xinggang Wang.
national conference on artificial intelligence (2017)

425 Citations

DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection

Wei Shen;Xinggang Wang;Yan Wang;Xiang Bai.
computer vision and pattern recognition (2015)

422 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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