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D-Index & Metrics

Computer Science

D-Index
57
Citations
15647
World Ranking
3784
National Ranking
1804

Overview

Cha Zhang is a researcher affiliated with Microsoft in the United States. Their academic work primarily spans the field of Computer Science, with a specific focus on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Physiology, Atomic and Molecular Physics and Optics, and Media Technology.

The main topics addressed in Zhang's research include:

  • Handwritten Text Recognition Techniques
  • Natural Language Processing Techniques
  • Adipose Tissue and Metabolism
  • Multimodal Machine Learning Applications
  • Topic Modeling
  • Quantum Computing Algorithms and Architecture
  • Quantum Information and Cryptography

Zhang has contributed to several recent publications, including:

  • Strong Quantum Computational Advantage Using a Superconducting Quantum Processor, 2021, Physical Review Letters
  • TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models, 2023, Proceedings of the AAAI Conference on Artificial Intelligence
  • Quantum computational advantage via 60-qubit 24-cycle random circuit sampling, 2021, Science Bulletin
  • DiT: Self-supervised Pre-training for Document Image Transformer, 2022, Proceedings of the 30th ACM International Conference on Multimedia
  • Exercise-induced α-ketoglutaric acid stimulates muscle hypertrophy and fat loss through OXGR1-dependent adrenal activation, 2020, The EMBO Journal

The venues where Zhang frequently publishes include:

  • arXiv (Cornell University)
  • The EMBO Journal
  • EMBO Reports
  • Physical Review Letters
  • Proceedings of the AAAI Conference on Artificial Intelligence

Cha Zhang collaborates often with a group of co-authors, including:

  • Dinei Florêncio
  • Yijuan Lu
  • Tengchao Lv
  • Furu Wei
  • Yexian Yuan

Best Publications

  • Ensemble Machine Learning: Methods and Applications

    Cha Zhang;Yunqian Ma

  • Multiple Instance Boosting for Object Detection

    Cha Zhang;John C. Platt;Paul A. Viola

  • Training deep networks for facial expression recognition with crowd-sourced label distribution

    Emad Barsoum;Cha Zhang;Cristian Canton Ferrer;Zhengyou Zhang

  • A Survey of Recent Advances in Face Detection

    Cha Zhang;Zhengyou Zhang

  • Automatic speech emotion recognition using recurrent neural networks with local attention

    Seyedmahdad Mirsamadi;Emad Barsoum;Cha Zhang

  • Image based Static Facial Expression Recognition with Multiple Deep Network Learning

    Zhiding Yu;Cha Zhang

  • Efficient feature extraction for 2D/3D objects in mesh representation

    Cha Zhang;Tsuhan Chen

  • A survey on face detection in the wild

    Stefanos Zafeiriou;Cha Zhang;Zhengyou Zhang

  • LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding

    Yang Xu;Yiheng Xu;Tengchao Lv;Lei Cui

  • Multiview Imaging and 3DTV

    Akira Kubota;Aljoscha Smolic;Marcus Magnor;Masayuki Tanimoto

  • A survey on image-based rendering—representation, sampling and compression

    Cha Zhang;Tsuhan Chen

  • An active learning framework for content-based information retrieval

    Cha Zhang;Tsuhan Chen

  • Improving multiview face detection with multi-task deep convolutional neural networks

    Cha Zhang;Zhengyou Zhang

  • Maximum Likelihood Sound Source Localization and Beamforming for Directional Microphone Arrays in Distributed Meetings

    Cha Zhang;D. Florencio;D.E. Ba;Zhengyou Zhang

  • CROWDMOS: An approach for crowdsourcing mean opinion score studies

    Flavio Ribeiro;Dinei Florencio;Cha Zhang;Michael Seltzer

  • Point cloud attribute compression with graph transform

    Cha Zhang;Dinei Florencio;Charles Loop

  • Calibration between depth and color sensors for commodity depth cameras

    Cha Zhang;Zhengyou Zhang

  • 3D deformable face tracking with a commodity depth camera

    Qin Cai;David Gallup;Cha Zhang;Zhengyou Zhang

  • A self-reconfigurable camera array

    Cha Zhang;Tsuhan Chen

  • Emotion recognition in the wild from videos using images

    Sarah Adel Bargal;Emad Barsoum;Cristian Canton Ferrer;Cha Zhang

  • Multiple-Instance Pruning For Learning Efficient Cascade Detectors

    Cha Zhang;Paul A. Viola

Frequent Co-Authors

Zhengyou Zhang
Zhengyou Zhang Tencent (China)
Dinei Florencio
Dinei Florencio Microsoft (United States)
Tsuhan Chen
Tsuhan Chen Cornell University
Yong Rui
Yong Rui Lenovo (China)
Philip A. Chou
Philip A. Chou Google (United States)
Jin Li
Jin Li Microsoft (United States)
Paul A. Viola
Paul A. Viola Microsoft (United States)
Zicheng Liu
Zicheng Liu Microsoft (United States)
Ross Cutler
Ross Cutler Microsoft (United States)
Ruigang Yang
Ruigang Yang University of Kentucky

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