Instance-Level Recognition Workshop at ECCV'22

ILR2022

Visual instance-level recognition and retrieval are fundamental tasks in computer vision. Despite the recent advances in this field, many techniques have been evaluated on a limited number of domains, with small number of classes. We believe that the research community can benefit from a new suite of datasets and associated challenges, to improve the understanding about the limitations of current technology, and with an opportunity to introduce new techniques.

This year, we propose the first Universal Image Embedding Challenge, where the goal is to develop image representations that work well across several domains combined.

The Instance-Level Recognition (ILR) Workshop is a follow-up of four successful editions of our previous workshops — the first two having focused only on landmark recognition (CVPRW18, CVPRW19), and the latest two expanded to two extra domains (artworks and products) (ECCVW20, ICCVW21).

Workshop Topics

Universal Image Embedding Challenge

Large-scale evaluation of universal embedding models. The data comprises imagery from several domains, e.g., landmarks, artworks, cars, furniture, apparel, toys and storefronts.
Challenge Website

Language Assisted Product Retrieval Challenge

Multi-modal product recognition using a product image and user textual feedback to find the product instance fulfilling the user’s request.


Invited Speakers

Minsu Cho

Associate Professor of CSE & AI, POSTECH

Vicente Ordonez

Associate Professor of Computer Science, Rice University

Mathilde Caron

Research Scientist at Google Research


Organizers

Andre Araujo

Google Research (Primary Contact)

Bingyi Cao

Google Research

Ondrej Chum

Czech Technical University

Noa Garcia

Osaka University

Bohyung Han

Seoul National University

Shih-Fu Chang

Columbia University

Guangxing Han

Columbia University

Pradeep Natarajan

Amazon Alexa

Giorgos Tolias

Czech Technical University

Tobias Weyand

Google Research

Xu Zhang

Amazon Alexa

Torsten Sattler

Czech Technical University

Sanqiang Zhao

Amazon Alexa

© 2022 ILR2022

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