H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 48 Citations 52,752 150 World Ranking 3099 National Ranking 1628

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Artificial intelligence, Object, Pattern recognition, Cognitive neuroscience of visual object recognition and Computer vision are his primary areas of study. His studies link Machine learning with Artificial intelligence. Hao Su has included themes like Network architecture and Benchmark in his Object study.

His study in Cognitive neuroscience of visual object recognition is interdisciplinary in nature, drawing from both Object detection and Field. His Field research integrates issues from Contextual image classification, Discriminative model and Categorical variable. He has researched Categorical variable in several fields, including Data science and Pattern recognition.

His most cited work include:

  • ImageNet Large Scale Visual Recognition Challenge (18266 citations)
  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (3318 citations)
  • PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2476 citations)

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

Artificial intelligence, Computer vision, Pattern recognition, Object and Point cloud are his primary areas of study. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning. His research in Machine learning intersects with topics in Contextual image classification and Benchmark.

In general Pattern recognition, his work in Convolutional neural network is often linked to Set linking many areas of study. The Object study combines topics in areas such as Representation and Image. His research integrates issues of Object detection and Field in his study of Cognitive neuroscience of visual object recognition.

He most often published in these fields:

  • Artificial intelligence (61.00%)
  • Computer vision (22.50%)
  • Pattern recognition (19.50%)

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

  • Artificial intelligence (61.00%)
  • Point cloud (16.50%)
  • Computer vision (22.50%)

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

His main research concerns Artificial intelligence, Point cloud, Computer vision, Reinforcement learning and Rendering. His Artificial intelligence research incorporates elements of Generalizability theory and Pattern recognition. His work carried out in the field of Generalizability theory brings together such families of science as Object, Natural language processing, Machine learning and Code refactoring.

His Point cloud research is multidisciplinary, incorporating perspectives in Data mining, Representation, Surface reconstruction, Code and Point. His Computer vision research includes elements of Field, Translation and Radiance. His studies deal with areas such as Robot, Heuristic, Task and Benchmark as well as Reinforcement learning.

Between 2019 and 2021, his most popular works were:

  • SAPIEN: A SimulAted Part-Based Interactive ENvironment (38 citations)
  • Supramolecular prodrug hydrogelator as an immune booster for checkpoint blocker-based immunotherapy. (20 citations)
  • Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness (18 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Hao Su focuses on Artificial intelligence, Cancer research, Camptothecin, Reinforcement learning and Prodrug. Artificial intelligence and Pattern recognition are commonly linked in his work. His Cancer research research focuses on Self-healing hydrogels and how it relates to Ex vivo and Tumor penetration.

His Reinforcement learning research is multidisciplinary, incorporating elements of Imitation learning and Combinatorics. His Prodrug research is multidisciplinary, relying on both Immune checkpoint, Chemoimmunotherapy, Immunotherapy and Tumor microenvironment. His Feature extraction research integrates issues from Artificial neural network, Image resolution, Joint and Consistency.

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.

Best Publications

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause.
International Journal of Computer Vision (2015)

17815 Citations

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

R. Qi Charles;Hao Su;Mo Kaichun;Leonidas J. Guibas.
computer vision and pattern recognition (2017)

2948 Citations

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Charles Ruizhongtai Qi;Li Yi;Hao Su;Leonidas J. Guibas.
neural information processing systems (2017)

1695 Citations

ShapeNet: An Information-Rich 3D Model Repository

Angel X. Chang;Thomas A. Funkhouser;Leonidas J. Guibas;Pat Hanrahan.
arXiv: Graphics (2015)

1236 Citations

Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

Li-jia Li;Hao Su;Li Fei-fei;Eric P. Xing.
neural information processing systems (2010)

1081 Citations

Volumetric and Multi-view CNNs for Object Classification on 3D Data

Charles R. Qi;Hao Su;Matthias NieBner;Angela Dai.
computer vision and pattern recognition (2016)

844 Citations

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Charles R. Qi;Hao Su;Kaichun Mo;Leonidas J. Guibas.
arXiv: Computer Vision and Pattern Recognition (2016)

620 Citations

A Point Set Generation Network for 3D Object Reconstruction from a Single Image

Haoqiang Fan;Hao Su;Leonidas Guibas.
computer vision and pattern recognition (2017)

555 Citations

Frustum PointNets for 3D Object Detection from RGB-D Data

Charles R. Qi;Wei Liu;Chenxia Wu;Hao Su.
computer vision and pattern recognition (2018)

554 Citations

Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

Hao Su;Charles R. Qi;Yangyan Li;Leonidas J. Guibas.
international conference on computer vision (2015)

554 Citations

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Best Scientists Citing Hao Su

Leonidas J. Guibas

Leonidas J. Guibas

Stanford University

Publications: 103

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 98

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 98

Yi Yang

Yi Yang

Zhejiang University

Publications: 98

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 88

Li Fei-Fei

Li Fei-Fei

Stanford University

Publications: 87

Silvio Savarese

Silvio Savarese

Stanford University

Publications: 84

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 79

Trevor Darrell

Trevor Darrell

University of California, Berkeley

Publications: 79

Vittorio Ferrari

Vittorio Ferrari

Google (United States)

Publications: 75

Raquel Urtasun

Raquel Urtasun

University of Toronto

Publications: 75

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 71

Tao Mei

Tao Mei

Jingdong (China)

Publications: 69

Andrea Vedaldi

Andrea Vedaldi

University of Oxford

Publications: 68

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 67

Abhinav Gupta

Abhinav Gupta

Facebook (United States)

Publications: 65

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