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Computer Science

D-Index
35
Citations
6250
World Ranking
11579
National Ranking
4756

Overview

Adrien Gaidon is affiliated with Stanford University in the United States. Their research primarily spans the fields of computer science and engineering, with a particular focus on computer vision and pattern recognition, artificial intelligence, automotive engineering, aerospace engineering, and media technology.

Their work explores advanced topics within these fields, including:

  • Advanced Vision and Imaging
  • Optical measurement and interference techniques
  • Advanced Neural Network Applications
  • Autonomous Vehicle Technology and Safety
  • Robotics and Sensor-Based Localization
  • Video Surveillance and Tracking Methods
  • Image Processing Techniques and Applications

Adrien Gaidon has contributed extensively to several prominent publication venues. Frequent venues include:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Lecture Notes in Computer Science

Among their recent publications are:

  • Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction, 2020, IEEE Robotics and Automation Letters
  • Differentiable Rendering: A Survey, 2020, arXiv (Cornell University)
  • Revisiting the "Video" in Video-Language Understanding, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Semantically-Guided Representation Learning for Self-Supervised Monocular Depth, 2020, arXiv (Cornell University)
  • Multi-Frame Self-Supervised Depth with Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adrien Gaidon frequently collaborates with several co-authors in their research. Regular collaborators include Rareş Ambruş, Vitor Guizilini, Igor Vasiljevic, Kuan-Hui Lee, and Pavel Tokmakov.

Best Publications

  • VirtualWorlds as Proxy for Multi-object Tracking Analysis

    Adrien Gaidon;Qiao Wang;Yohann Cabon;Eleonora Vig

  • Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

    Kaidi Cao;Colin Wei;Adrien Gaidon;Nikos Arechiga

  • 3D Packing for Self-Supervised Monocular Depth Estimation

    Vitor Guizilini;Rares Ambrus;Sudeep Pillai;Allan Raventos

  • Virtual Worlds as Proxy for Multi-Object Tracking Analysis

    Adrien Gaidon;Qiao Wang;Yohann Cabon;Eleonora Vig

  • Exploring the Limitations of Behavior Cloning for Autonomous Driving

    Felipe Codevilla;Eder Santana;Antonio Lopez;Adrien Gaidon

  • It Is Not the Journey But the Destination: Endpoint Conditioned Trajectory Prediction

    Karttikeya Mangalam;Harshayu Girase;Shreyas Agarwal;Kuan-Hui Lee

  • Spatio-Temporal Graph for Video Captioning With Knowledge Distillation

    Boxiao Pan;Haoye Cai;De-An Huang;Kuan-Hui Lee

  • ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape

    Fabian Manhardt;Wadim Kehl;Adrien Gaidon

  • Temporal Localization of Actions with Actoms

    A. Gaidon;Z. Harchaoui;C. Schmid

  • Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

    Kaidi Cao;Colin Wei;Adrien Gaidon;Nikos Arechiga

  • Actom sequence models for efficient action detection

    Adrien Gaidon;Zaid Harchaoui;Cordelia Schmid

  • SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation

    Sudeep Pillai;Rares Ambrus;Adrien Gaidon

  • Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

    Bingbin Liu;Ehsan Adeli;Zhangjie Cao;Kuan-Hui Lee

  • Revisiting the “Video” in Video-Language Understanding

    Unknown

  • Learning to Fuse Things and Stuff

    Jie Li;Allan Raventos;Arjun Bhargava;Takaaki Tagawa

  • Activity representation with motion hierarchies

    Adrien Gaidon;Zaid Harchaoui;Cordelia Schmid

  • Differentiable Rendering: A Survey.

    Hiroharu Kato;Deniz Beker;Mihai Morariu;Takahiro Ando

  • Procedural Generation of Videos to Train Deep Action Recognition Networks

    Cesar Roberto de Souza;Adrien Gaidon;Yohann Cabon;Antonio Manuel Lopez

  • Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

    Unknown

  • INRIA-LEARs video copy detection system

    Matthijs Douze;Adrien Gaidon;Hervé Jégou;Marcin Marszałek

  • Recognizing activities with cluster-trees of tracklets

    Adrien Gaidon;Zaid Harchaoui;Cordelia Schmid

  • Multi-Frame Self-Supervised Depth with Transformers

    Unknown

  • Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

    Vitor Guizilini;Rui Hou;Jie Li;Rares Ambrus

  • Autolabeling 3D Objects With Differentiable Rendering of SDF Shape Priors

    Sergey Zakharov;Wadim Kehl;Arjun Bhargava;Adrien Gaidon

  • Real-Time Panoptic Segmentation From Dense Detections

    Rui Hou;Jie Li;Arjun Bhargava;Allan Raventos

  • Towards Zero-Shot Scale-Aware Monocular Depth Estimation

    Unknown

  • Exploring the Limitations of Behavior Cloning for Autonomous Driving

    Felipe Codevilla;Eder Santana;Antonio M. López;Adrien Gaidon

  • SPIGAN: Privileged Adversarial Learning from Simulation

    Kuan-Hui Lee;German Ros;Jie Li;Adrien Gaidon

Frequent Co-Authors

Cordelia Schmid
Cordelia Schmid French Institute for Research in Computer Science and Automation - INRIA
Wolfram Burgard
Wolfram Burgard University of Technology Nuremberg
Ehsan Adeli
Ehsan Adeli Stanford University
Antonio M. López
Antonio M. López Autonomous University of Barcelona
Zaid Harchaoui
Zaid Harchaoui University of Washington
Tengyu Ma
Tengyu Ma Stanford University
Mac Schwager
Mac Schwager Stanford University
Marco Pavone
Marco Pavone Stanford University

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