Uniud Submission To The Epic-kitchens-100 Multi-instance Retrieval Challenge 2023
2023 · Alex Falcon, Giuseppe Serra
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.
Authors
(none)
Tags
Stats
Related papers
- Uniud-fbk-ub-unibz Submission To The Epic-kitchens-100 Multi-instance Retrieval Challenge 2022 (2022)0.00
- Symmetric Multi-similarity Loss For EPIC-KITCHENS-100 Multi-instance Retrieval Challenge 2024 (2024)1.20
- Exploiting Semantic Role Contextualized Video Features For Multi-instance Text-video Retrieval EPIC-KITCHENS-100 Multi-instance Retrieval Challenge 2022 (2022)0.00
- Contextrefine-clip For EPIC-KITCHENS-100 Multi-instance Retrieval Challenge 2025 (2025)0.95
- Egocentric Video-language Pretraining @ EPIC-KITCHENS-100 Multi-instance Retrieval Challenge 2022 (2022)4.83
- Google Landmark Retrieval 2021 Competition Third Place Solution (2021)0.00
- 1st Place Solution To Google Landmark Retrieval 2020 (2020)0.00
- Scaling Multilingual Semantic Search In Uber Eats Delivery (2026)0.00