UniUD-FBK-UB-UniBZ Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022
Alex Falcon, Giuseppe Serra, Sergio Escalera, Oswald Lanz

TL;DR
This paper details a model ensemble approach using relevance-augmented triplet loss for the EPIC-Kitchens-100 retrieval challenge, achieving competitive nDCG and mAP scores.
Contribution
The paper introduces a novel ensemble of models trained with relevance-augmented triplet loss variants for multi-instance retrieval.
Findings
Achieved 61.02% nDCG on the leaderboard
Achieved 49.77% mAP on the leaderboard
Demonstrated effectiveness of relevance-augmented triplet loss
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHandwritten Text Recognition Techniques · Topic Modeling · Natural Language Processing Techniques
