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Paper detail

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

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.

preprint2022arXivOpen access
Alex FalconGiuseppe SerraSergio EscaleraOswald Lanz
Computer Vision
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Alex FalconGiuseppe SerraSergio EscaleraOswald Lanz

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