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

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