UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023
Alex Falcon, Giuseppe Serra

TL;DR
This paper details a submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023, involving model ensembling and achieving competitive retrieval scores on the leaderboard.
Contribution
The paper introduces a model ensembling approach trained with different loss functions for multi-instance retrieval in egocentric videos.
Findings
Achieved 56.81% nDCG score
Achieved 42.63% mAP score
Utilized ensembling of models trained on limited data
Abstract
In this report, we present the technical details of our submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023. To participate in the challenge, we ensembled two models trained with two different loss functions on 25% of the training data. Our submission, visible on the public leaderboard, obtains an average score of 56.81% nDCG and 42.63% mAP.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Topic Modeling · Natural Language Processing Techniques
