Uniud-fbk-ub-unibz Submission To The Epic-kitchens-100 Multi-instance Retrieval Challenge 2022
Alex Falcon, Giuseppe Serra, Sergio Escalera, Oswald Lanz . Arxiv 2022 – 1 citation
[Paper]
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.