UniUD-FBK-UB-UniBZ Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022. (arXiv:2206.10903v1 [cs.CV])

This report presents the technical details of our submission to the
EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022. To participate in
the challenge, we designed an ensemble consisting of different models trained
with two recently developed relevance-augmented versions of the widely used
triplet loss. Our submission, visible on the public leaderboard, obtains an
average score of 61.02% nDCG and 49.77% mAP.

arxiv challenge cv retrieval