Second Landmark Recognition Workshop

LandmarksCVPRW19

This workshop fosters research on image retrieval and landmark recognition by introducing a novel large-scale dataset, together with evaluation protocols. Here is a Google AI blog detailing the workshop and the challenge. Our 1st workshop is held in CVPR 2018.

Recognition Challenge

https://www.kaggle.com/c/landmark-recognition-2019

Label famous (and not-so-famous) landmarks in image.

Retrieval Challenge

https://www.kaggle.com/c/landmark-retrieval-2019

Given an image, can you find all of the same landmarks in a dataset?


Challenge Timeline

Challenge Begins

Challenge is held on Kaggle

Apr. 8 2019

Final Submission Deadline

Jun. 3 2019

Workshop

Held with CVPR’19

Jun. 16 2019, 8:30 - 12:30, PDT

Workshop Schedule

Welcome Remarks

(Hosted by Andre Araujo) Link

8:45 - 8:55

Invited Talk 1

Noah Snavely (Cornell Tech / Google AI)
End-to-End Geometric Learning

(Hosted by Jack Sim) Link

8:55 - 9:30

Dataset and Challenge Overview

(Hosted by Tobias Weyand and Bingyi Cao) Link

9:30 - 10:00

Coffee Break

10:00 - 10:30

Recognition Challenge Winner Presentations

JL , GLRunner , smlyaka (Hosted by Bohyung Han) 10:30 - 11:10

Retrieval Challenge Winner Presentations

smlyaka , imagesearch , Layer 6 AI (Hosted by Xu Zhang) 11:10 - 11:50

Invited Talk 2

Krystian Mikolajczyk (Imperial College London)
Recent Progress and Remaining Challenges in Local Features

11:50 - 12:25

Close Remark

12:25 - 12:35

Recognition Challenge Participant Slides

Link

Retrieval Challenge Participant Slides

Link

Workshop Photos

Link

Invited Speakers

Noah Snavely

Associate Professor at Cornell Tech

End-to-End Geometric Learning

One often hears that vision systems should be trained end-to-end using deep learning. This talk will explore this idea in the context of 3D geometry, presenting end-to-end methods for a number of tasks, including keypoint detection, pose estimation, and view synthesis. I will show how the use of geometric reasoning as an end goal of learning can enable emergent discovery of good keypoints, systems for predicting 3D shape from single images, and more, all without the use of explicit supervision. I will relate these ideas back to the landmark recognition problem.

Krystian Mikolajczyk

Associate Professor at Imperial College London

Recent Progress and Remaining Challenges in Local Features

In many computer vision applications local image features and descriptors have been replaced by end-to-end learning based methods but still remain the preferred choice for estimating accurate 3D models in multiple view geometry, camera and object pose estimation, or efficient SLAM. Large benchmarks and synthetically generated training data stimulated the progress in nearly all areas of computer vision including keypoint detection and description. I will present some recent works in this domain including new multi-task benchmarks for feature matching and localization, new methods for keypoint extraction and description, as well as for improving their efficiency.


Organizers

Bohyung Han

Associate Professor, Seoul National University (Primary Contact)

Andre Araujo

Software Engineer, Google (Primary Contact)

Bingyi Cao

Software Engineer, Google

Shih-Fu Chang

Professor, Columbia University

Ondrej Chum

Associate Professor, Czech Technical University

Torsten Sattler

Associate Professor, Chalmers University of Technology

Jack Sim

Software Engineer, Google

Giorgos Tolias

Postdoc, Czech Technical University

Tobias Weyand

Software Engineer, Google

Xu Zhang

Postdoc, Columbia University

© 2019 Landmarks19

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