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UniUD-FBK-UB-UniBZ Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022

2022-06-22 08:16:04
Alex Falcon, Giuseppe Serra, Sergio Escalera, Oswald Lanz
arXiv_CV
 
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2206.10903

PDF

https://arxiv.org/pdf/2206.10903.pdf

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