1st eProduct Visual Search Challenge Call for Papers

Dear Colleague,


I am very excited to announce that the 1st eProduct Visual Search
Challenge is now live! "eProduct" is a challenging million-scale image
dataset for fine-grained product recognition tasks with only coarse
categorical labels and image titles. Please join this part of the
journey towards discovering solutions for fine-grained product
recognition.


The Task: Large-scale product recognition is one of the major
applications of computer vision and machine learning in the e-commerce
domain. Since the number of products is typically much larger than the
number of categories of products, image-based product recognition is
often cast as a visual search rather than a classification problem. It
is also one of the instances of super fine-grained recognition, where
there are many products with slight or subtle visual differences. It
has always been a challenge to create a benchmark dataset for training
and evaluation on various visual search solutions in a real-world
setting. This motivated creation of eProduct, a dataset consisting of
2.5 million product images towards accelerating development in the
areas of self-supervised learning, weakly-supervised learning, and
multimodal learning, for fine-grained recognition.

eProduct consists of a training dataset and an evaluation dataset. The
training set contains 1.3M+ listing images, with titles and
hierarchical category labels. The evaluation set includes 5K query and
1.1M+ index images for visual search evaluation. The high-level
structure of eProduct is shown in the following figure.

You are asked to develop models and algorithms to retrieve the
same products (see below) from a million-scale product database
given a query image. The ideal solution will retrieve the
same products as the top items in your rankings. Results will
be evaluated by the maco-average "recall@10" score based on our ground
truth benchmark data.


Important Dates:

    16th April 2021: Beginning of Dev Phase (open for requesting data access)
    27th May 2021: Dev Phase ends. Moreover, it is the last day to update or merge on teams, and to request data access.
    28th May 2021: Beginning of the Test Phase (release new query data)
    5th June 2021: Test Phase ends.
    12th June 2021: Deadline to submit codes and tech report about final solutions.
    15th June 2021: Final ranking disclosed and winning teams announced.
    25th June 2021: Workshop day! Winners may present their method depending on review decisions. 

The data challenge is sponsored by eBay and part of the FGVC8 workshop
at CVPR 2021. Please read more information on the EvalAI challenge
page.


Best regards,

Jiangbo Yuan
eBay CV Research