Exploiting Semantic Role Contextualized Video Features for Multi-Instance Text-Video Retrieval EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
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Abstract (arXiv)
In this report, we present our approach for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022. We first parse sentences into semantic roles corresponding to verbs and nouns; then utilize self-attentions to exploit semantic role contextualized video features along with textual features via triplet losses in multiple embedding spaces. Our method overpasses the strong baseline in normalized Discounted Cumulative Gain (nDCG), which is more valuable for semantic similarity. Our submission is ranked 3rd for nDCG and ranked 4th for mAP.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 15 references that are in the map. First posted on arXiv 29 Jun 2022.
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12 of 15 references.
References · 12
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Citing papers per year: 2023: 3; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2025Seeing Culture: A Benchmark for Visual Reasoning and Groundingcited by 8
- 2023An Overview of Challenges in Egocentric Text-Video Retrievalcited by 1
- 2023UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023cited by 0
- 2023Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Interventioncited by 0
- 2026Cultural Moment Benchmark: Evaluating Video Cultural Reasoning and Grounding in Southeast Asiacited by 0
- 2026Cultural Moment Benchmark: Evaluating Video Cultural Reasoning and Grounding in Southeast Asia24 Aug 2026
- 2025Seeing Culture: A Benchmark for Visual Reasoning and Grounding20 Sep 2025
- 2023Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention17 Sep 2023
- 2023UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 202327 Jun 2023
- 2023An Overview of Challenges in Egocentric Text-Video Retrieval7 Jun 2023