Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge
  for Generic Image Representations
Nikolaos-Antonios Ypsilantis Kaifeng Chen Bingyi Cao Mário Lipovský Pelin Dogan-Schönberger Grzegorz Makosa Boris Bluntschli Mojtaba Seyedhosseini Ondřej Chum André Araujo
Fine-grained and instance-level recognition methods are commonly trained and
evaluated on specific domains, in a model per domain scenario. Such an
approach, however, is impractical in real large-scale applications. In this
work, we address the problem of universal image embedding, where a single
universal model is trained and used in multiple domains. First, we leverage
existing domain-specific datasets to carefully construct a new large-scale
public benchmark for the evaluation of universal image embeddings, with 241k
query images, 1.4M index images and 2.8M training images across 8 different
domains and 349k classes. We define suitable metrics, training and evaluation
protocols to foster future research in this area. Second, we provide a
comprehensive experimental evaluation on the new dataset, demonstrating that
existing approaches and simplistic extensions lead to worse performance than an
assembly of models trained for each domain separately. Finally, we conducted a
public research competition on this topic, leveraging industrial datasets,
which attracted the participation of more than 1k teams worldwide. This
exercise generated many interesting research ideas and findings which we
present in detail. Project webpage: https://cmp.felk.cvut.cz/univ_emb/