A great deal of work aims to discover large general purpose models of image interest or
memorability for visual search and information retrieval. This paper argues that image …

Many image retrieval studies use metric learning to train an image encoder. However, metric
learning cannot handle differences in users' preferences, and requires data to train an …

Image retrieval has achieved remarkable improvements with the rapid progress on visual
representation and indexing techniques. Given a query image, search engines are expected …

One of the fundamental problems in image search is to rank image documents according to
a given textual query. Existing search engines highly depend on surrounding texts for …

One fundamental problem in image search is to learn the ranking functions (ie, the similarity
between query and image). Recent progress on this topic has evolved through two …

In e-commerce, users' feedback may vary depending on how the information they encounter
is structured. Recently, ranking approaches based on deep learning successfully provided …

Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@ k. Yet,
those metrics, are limited to binary labels and do not take into account errors' severity. This …

Our objective is to estimate the relevance of an image to a query for image search purposes.
We address two limitations of the existing image search engines in this paper. First, there is …

We introduce an information-theoretic loss function, RankMI, and an associated training
algorithm for deep representation learning for image retrieval. Our proposed framework …

An interactive image retrieval system learns which images in the database belong to a user's
query concept, by analyzing the example images and feedback provided by the user. The …