mlsanityData to 14 Oct 2026

UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023

arXiv 2306.15445 ↗First posted 27 Jun 2023cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 14 references that are in the map. First posted on arXiv 27 Jun 2023.

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References · 12

  1. 2020Rescaling Egocentric Visioncited by 540
  2. 2019EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognitioncited by 226
  3. 2020Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoningcited by 180
  4. 2019Stochastic Optimization of Sorting Networks via Continuous Relaxationscited by 111
  5. 2019Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddingscited by 101
  6. 2021On Semantic Similarity in Video Retrievalcited by 46
  7. 2021NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sortingcited by 41
  8. 2022A Feature-space Multimodal Data Augmentation Technique for Text-video Retrievalcited by 13
  9. 2022Learning video retrieval models with relevance-aware online miningcited by 6
  10. 2022Exploiting Semantic Role Contextualized Video Features for Multi-Instance Text-Video Retrieval EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022cited by 5
  11. 2022Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022cited by 2
  12. 2022UniUD-FBK-UB-UniBZ Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022cited by 1

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