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 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 (, ), and the latest two expanded to two extra domains (artworks and products) (, ).
Large-scale evaluation of universal embedding models. The data comprises imagery from several domains, e.g., landmarks, artworks, cars, furniture, apparel, toys and storefronts.
Multi-modal product recognition using a product image and user textual feedback to find the product instance fulfilling the user’s request.
Google Research (Primary Contact)
Google Research
Czech Technical University
Osaka University
Seoul National University
Columbia University
Columbia University
Amazon Alexa
Czech Technical University
Google Research
Amazon Alexa
Czech Technical University
Amazon Alexa
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