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David J. Crandall

David J. Crandall

D-Index & Metrics

Computer Science

D-Index
55
Citations
14175
World Ranking
4259
National Ranking
2006

Overview

David J. Crandall is affiliated with Indiana University in the United States. Their research focuses primarily on computer science, with a significant concentration in the subfields of computer vision and pattern recognition, artificial intelligence, sociology and political science, molecular biology, and signal processing.

The main topics covered in their work include:

  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Domain Adaptation and Few-Shot Learning
  • Video Surveillance and Tracking Methods
  • Multimodal Machine Learning Applications
  • AI-based Problem Solving and Planning
  • Advanced Image and Video Retrieval Techniques

Their recent publications demonstrate a consistent engagement with cutting-edge research in computer vision and related fields. Notable papers include:

  • "Ego4D: Around the World in 3,000 Hours of Egocentric Video" (2022) published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "A Survey on Deep Learning Technique for Video Segmentation" (2022) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Zero-Shot Video Object Segmentation with Co-Attention Siamese Networks" (2020) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Segmenting Objects From Relational Visual Data" (2021) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Plasmonic Anticounterfeit Tags with High Encoding Capacity Rapidly Authenticated with Deep Machine Learning" (2021) published in ACS Nano

Frequent co-authors in their research collaborations include:

  • David Leake
  • Weslie Khoo
  • Jianbing Shen
  • Wenguan Wang
  • Zachary Wilkerson

Their publications are often featured in venues such as arXiv (Cornell University), IEEE Transactions on Pattern Analysis and Machine Intelligence, Clinical Chemistry, ACM Transactions on Computer-Human Interaction, and the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Best Publications

  • Mapping the world's photos

    David J. Crandall;Lars Backstrom;Daniel Huttenlocher;Jon Kleinberg

  • Feedback effects between similarity and social influence in online communities

    David Crandall;Dan Cosley;Daniel Huttenlocher;Jon Kleinberg

  • Inferring social ties from geographic coincidences

    David J. Crandall;Lars Backstrom;Dan Cosley;Siddharth Suri

  • Ego4D: Around the World in 3,000 Hours of Egocentric Video

    Kristen Grauman;Andrew Westbury;Eugene Byrne;Zachary Chavis

  • Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions

    Sven Bambach;Stefan Lee;David J. Crandall;Chen Yu

  • Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models.

    Ashwin K. Vijayakumar;Michael Cogswell;Ramprasaath R. Selvaraju;Qing Sun

  • Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification

    Mang Ye;Jianbing Shen;David J. Crandall;Ling Shao

  • Spatial priors for part-based recognition using statistical models

    D. Crandall;P. Felzenszwalb;D. Huttenlocher

  • Discrete-continuous optimization for large-scale structure from motion

    David Crandall;Andrew Owens;Noah Snavely;Dan Huttenlocher

  • Landmark classification in large-scale image collections

    Yunpeng Li;David J. Crandall;Daniel P. Huttenlocher

  • Discovering localized attributes for fine-grained recognition

    Kun Duan;Devi Parikh;David Crandall;Kristen Grauman

  • Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

    Wenguan Wang;Xiankai Lu;Jianbing Shen;David Crandall

  • Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

    Stefan Lee;Senthil Purushwalkam;Michael Cogswell;David J. Crandall

  • Weakly supervised learning of part-based spatial models for visual object recognition

    David J. Crandall;Daniel P. Huttenlocher

  • Privacy behaviors of lifeloggers using wearable cameras

    Roberto Hoyle;Robert Templeman;Steven Armes;Denise Anthony

  • Diverse Beam Search for Improved Description of Complex Scenes

    Ashwin K. Vijayakumar;Michael Cogswell;Ramprasaath R. Selvaraju;Qing Sun

  • A Survey on Deep Learning Technique for Video Segmentation.

    Wenguan Wang;Tianfei Zhou;Fatih Porikli;David J. Crandall

  • Temporal Recurrent Networks for Online Action Detection

    Mingze Xu;Mingfei Gao;Yi-Ting Chen;Larry Davis

  • Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles

    Jangwon Lee;Jingya Wang;David Crandall;Selma Sabanovic

  • HOPE-Net: A Graph-Based Model for Hand-Object Pose Estimation

    Bardia Doosti;Shujon Naha;Majid Mirbagheri;David J. Crandall

  • SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion

    David J. Crandall;Andrew Owens;Noah Snavely;Daniel P. Huttenlocher

  • PlaceAvoider: Steering First-Person Cameras away from Sensitive Spaces.

    Robert Templeman;Mohammed Korayem;David J. Crandall;Apu Kapadia

  • Deepdiary: Lifelogging image captioning and summarization

    Chenyou Fan;Zehua Zhang;David J. Crandall

Frequent Co-Authors

Geoffrey C. Fox
Geoffrey C. Fox University of Virginia
Chen Yu
Chen Yu The University of Texas at Austin
John Paden
John Paden University of Kansas
Apu Kapadia
Apu Kapadia Indiana University
Stefan Lee
Stefan Lee Oregon State University
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
Jiebo Luo
Jiebo Luo University of Rochester
Linda B. Smith
Linda B. Smith Indiana University
Jianbing Shen
Jianbing Shen University of Macau

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